What's the best platform for deploying long-running AI agents?

Long-running AI agents need more than somewhere to run a container. They may need durable state, background workers, queues, databases, persistent storage, secure code execution, GPUs, and the ability to pause or recover without losing progress.

This article compares the leading deployment approaches and explains what enterprises, startups, and individual developers should evaluate when choosing a production platform.

The best platform depends on whether you need a complete production stack, durable orchestration, specialized compute, or a cloud-specific runtime.

• Best for complete agent infrastructure: Northflank. Run agents, APIs, workers, jobs, databases, queues, S3-compatible object storage or MinIO, sandboxes, and CPU or GPU workloads on Northflank Cloud or through self-serve BYOC.
• Best for managed agent orchestration: LangSmith Deployment. Deploy framework-agnostic agents with durable execution, persistent state, streaming, human-in-the-loop controls, and integrated tracing.
• Best AWS-native runtime: Amazon Bedrock AgentCore Runtime. Run isolated agent sessions with identity, tracing, persistent session-storage options, and dedicated microVM compute lifecycles of up to eight hours.
• Best for durable TypeScript background tasks: Trigger.dev. Run TypeScript agent tasks with retries, queues, waits, streaming, and human approval.
• Best for Python and GPU agents: Modal. Run elastic Functions and Sandboxes on CPU or GPU for up to 24 hours per execution.

Long-running agents create three different infrastructure requirements.

First, an agent may need active compute for minutes or hours while researching, processing files, generating code, or coordinating tools. Second, it may work briefly and then wait hours or days for a person, webhook, or another agent. Third, it may need to recover after a failure without repeating completed model calls or external side effects.

These requirements are not interchangeable. A persistent filesystem preserves files but does not record which tool should run next. A durable workflow engine can checkpoint logical progress but may still rely on another platform for workers, databases, model servers, networking, and GPUs.

The useful criteria cover both the agent run and the production system around it.

1. Recovery: Can the agent resume completed work after failure or suspension?
2. Background execution: Can work continue after the client receives a response?
3. Runtime flexibility: Can you use custom containers, frameworks, models, jobs, and GPUs?
4. Persistence: Where do files, agent state, databases, queues, and artifacts live?
5. Operations: Can teams inspect traces, logs, metrics, retries, and human interventions?
6. Deployment control: Can workloads run in your cloud account, VPC, or on-premises environment?
7. Enterprise readiness: Does the platform support identity, governance, compliance, private networking, and contractual requirements?

The products occupy different layers, so the table compares the operational responsibility each one owns.

These categories describe architectural fit rather than a universal ranking. Feature availability and limits vary by deployment model.

Northflank is the best fit when the agent must run as part of a production application rather than as an isolated invocation.

A production agent may need an API, background workers, managed databases, Redis or RabbitMQ, S3-compatible object storage or deployed MinIO, persistent volumes, and a private model server. Northflank runs those components in one control plane alongside one-off, scheduled, and externally triggered jobs.

Northflank sandboxes provide isolated ephemeral or persistent execution for agents that run commands or untrusted code. CPU and GPU workloads use the same deployment model, which lets an agent call privately hosted inference without splitting the application across separate platforms.

Like any general-purpose container platform, Northflank does not define the agent's checkpoint and replay semantics. Teams can pair it with LangGraph, Trigger.dev, Temporal, or application-managed checkpoints when runs must resume at a specific logical step.

Northflank combines enterprise governance and deployment controls with a self-service developer experience, serving enterprise platform teams, startups, and individual developers on the same platform.

Enterprise teams can apply SSO/SAML, SCIM, RBAC, audit logs, workload identities, secrets, network policies, and private networking across the agent and its supporting services. Self-serve BYOC deploys the data plane into the customer's AWS, GCP, Azure, Oracle, CoreWeave, or Civo account. BYOK extends the platform to compatible Kubernetes, on-premises, and bare-metal infrastructure.

For software vendors that need to run agents inside their customers' cloud environments, customer VPC deployments provide a consistent way to deploy and manage the same application across customer-owned AWS, GCP, Azure, and on-premises infrastructure.

Northflank is SOC 2 Type 2 certified and HIPAA compliant. Under an Enterprise contract, Northflank supports Business Associate Agreements for teams handling protected health information. Northflank Enterprise adds contractual SLAs, direct engineering support, infrastructure audit events, and options for stricter security, recovery, and deployment requirements.

For platform teams, this creates a governed self-service path instead of a collection of one-off agent deployments. Developers can ship through approved templates and workflows, while security teams retain control over identities, secrets, networks, workload placement, and the audit trail. The same Northflank APIs and deployment model apply across managed cloud, customer-owned cloud accounts, and imported Kubernetes clusters, which reduces the operational change involved in moving a successful agent from evaluation into a regulated production environment.

The same platform gives smaller teams a direct path from a repository to production.

Startups can use Northflank Cloud with Git-based builds, managed databases, secrets, TLS, CI/CD, and preview environments. Northflank remains self-service; Enterprise adds guarantees and controls to the same developer workflow.

Vibe coders can use these guides to take AI-generated applications from a coding tool to production on Northflank:

• How to deploy vibe-coded Claude Code apps to production
• How to deploy vibe-coded Lovable apps to production
• How to deploy vibe-coded Replit Agent apps to production
• How to deploy vibe-coded Cursor apps to production
• How to deploy vibe-coded Bolt.new apps to production

These platforms address specific parts of the deployment and orchestration stack.

• LangSmith Deployment: A framework-agnostic orchestration runtime with especially close LangGraph integration. It supports durable execution, persistent state, streaming, human-in-the-loop control, and cloud, hybrid, full self-hosted, or standalone Agent Server deployments.
• Amazon Bedrock AgentCore Runtime: An AWS-native option for isolated agent sessions, identity, tracing, MCP, A2A, and filesystem integrations. Each dedicated microVM compute lifecycle lasts up to eight hours, while a logical session can continue on new compute. Session storage can preserve filesystem data across stop and resume cycles; structured memory and workflow progress may still require AgentCore Memory or application-level storage.
• Trigger.dev: A durable task layer for TypeScript applications, with retries, queues, waits, scheduling, streaming, and human approval. It is available through Trigger.dev Cloud or self-hosting, although checkpoints, automatic scaling, and warm starts are Cloud-only.
• Modal: Elastic Python and GPU compute through serverless Functions and Sandboxes. Executions can run for up to 24 hours, while longer workloads need application-level checkpointing and recovery.

The best fit depends on the hardest operational requirement in your architecture.

Northflank is particularly well suited to the complete stack, enterprise governance, self-serve BYOC, GPUs, and a developer experience that also works for startups. LangSmith Deployment focuses on managed agent orchestration, AgentCore on AWS-native agent sessions, Trigger.dev on durable TypeScript background tasks, and Modal on Python or GPU compute.

For multi-component production agents, combine durable orchestration with full-stack runtime infrastructure rather than expecting one abstraction to provide both.

These answers cover the distinctions that most often change the deployment decision.

Not always. A short, read-only task may be safe to restart. Durable execution becomes important when a run includes costly model calls, non-repeatable side effects, unreliable tools, multi-agent coordination, or waits for human input.

No. It preserves files and artifacts but does not automatically record the current workflow step, completed side effects, or retry policy. Use an explicit state and recovery model alongside persistent storage.

Yes. Northflank can run agent APIs, background workers, scheduled jobs, databases, queues, S3-compatible object storage or MinIO, sandboxes, and CPU or GPU workloads under one operational model. This is useful when the agent is part of a larger production system rather than a standalone process.

Yes. Northflank's self-serve BYOC option lets teams deploy into their own AWS, GCP, Azure, Oracle, CoreWeave, or Civo account while using Northflank's developer experience and control plane. This supports data residency, private networking, infrastructure ownership, and enterprise governance requirements.

No. These tools manage workflow state, retries, and checkpoints, while Northflank runs the agent and its supporting infrastructure. Teams can deploy LangGraph, Trigger.dev workers, or other framework-based services on Northflank, or use the Temporal stack template to deploy Temporal with its supporting services preconfigured.

These Northflank resources cover the next architecture and implementation decisions.

• How to manage enterprise AI infrastructure: Connect agents, GPUs, governance, and cost control.
• How to secure AI agents in the cloud: Apply isolation, secrets, identities, and network boundaries.
• How to use agentic AI in your SDLC: Move agent output through previews and controlled releases.
• Best AI deployment platforms in 2026: Compare broader AI infrastructure categories.
• How to deploy vibe-coded apps: Take AI-generated code from localhost to production.