Best cloud sandboxes in 2026

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.

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.

The platforms and tools below cover the main approaches to cloud sandbox infrastructure available today. They are ordered from most comprehensive to most specialized.

Northflank is a workload platform that provides secure cloud 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.

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 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.

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.

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 runs the execution plane inside your own infrastructure with the same APIs.

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 supports all three.

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 imposes no session time limits and supports VPC deployment.

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, which supports both.

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.

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.

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.

Further reading to help you evaluate and implement the right cloud sandbox infrastructure for your use case.

• Top AI sandbox platforms, ranked
• Top BYOC AI sandboxes for running untrusted code
• What is a sandbox environment?
• What is an AI sandbox?
• How to spin up a secure code sandbox and microVM in seconds with Northflank
• E2B vs Modal vs Fly.io Sprites for AI code execution sandboxes
• Top Fly.io Sprites alternatives for secure AI code execution
• Best alternatives to E2B.dev for running untrusted code in secure sandboxes
• How to sandbox AI agents: microVMs, gVisor and isolation strategies
• Self-hosted AI sandboxes: guide to secure code execution