The best code execution sandbox for AI agents is one with persistence. Most comparison guides rank sandboxes by cold-start speed. For a real agent, this is often the wrong axis. A coding agent installs a dependency, writes a file, runs a test, reads the failure, edits, and runs again. Every step depends on the state the last step left behind. The best runtime is the one where work compounds instead of resetting.

This guide ranks sandboxes around statefulness first, then isolation, GPU support, and cost. Our verdict up front, by use case:

• Beam is the best choice for production agents that need persistent state and compute intensity together: gVisor isolation, GPU acceleration, stateful snapshots, and bring-your-own-compute, with no charge for cold-start or image-pull time. Best for long-horizon coding agents, agentic workflows, and compute-heavy tasks where the environment must survive across many actions.
• Modal is strong for serverless GPU jobs and batch inference with autoscaling, but it's managed-only and its sandbox compute is billed at roughly 3× its standard function rate.
• E2B offers clean AI-first SDKs and Firecracker microVM isolation, but caps sessions at 24 hours and has no GPU option.
• Daytona delivers the fastest cold starts (sub-90 ms) but defaults to container-level isolation, with microVMs only optional.
• Northflank is a complete platform (sandboxes plus databases, APIs, CI/CD) with microVM isolation, unlimited sessions, and the cheapest published CPU rate—best when you need infrastructure well beyond code execution.

If your agents do compute-intensive, multi-step work and you can't afford to rebuild the environment on every action, Beam is built for exactly that. Here's the full comparison.

What is a stateful code execution sandbox?

A code execution sandbox is an isolated environment where untrusted code—including code written or generated by an AI agent—runs without access to the host system or other workloads. It gives the agent a real operating system: a filesystem, network stack, package manager, and shell, all walled off from everything around it.

A stateful sandbox adds persistence between executions. Files written in one call are still there in the next. Installed packages stay installed. Processes keep running. Environment variables hold. The agent works against a continuous workspace rather than a fresh machine each time it acts.

That continuity is the line between a sandbox built for agents and one built to run a single snippet. See Beam's sandbox overview for how persistent environments are created and managed.

Why persistence matters for AI agents

An ephemeral runtime executes code and discards everything afterward—the same model as serverless functions like AWS Lambda, where each invocation is independent by design. A stateful runtime keeps the environment alive so work accumulates. The two fit different jobs:

Ephemeral fits one-shot execution. A single calculation or query, answer returned, nothing to carry forward.

Stateful fits multi-step, long-horizon work. A coding agent clones a repo, installs dependencies, edits files across turns, runs a build, inspects errors, iterates. On an ephemeral runtime it would re-clone and reinstall on every action.

The cost compounds. In a ten-step task, an ephemeral runtime repeats environment setup ten times; a stateful runtime pays it once. Beam removes the most common version of this tax directly: it doesn't bill for cold-start or container-image-pull time, so the setup an agent pays for once isn't re-charged on every spin-up.

Beam exposes this through persistent processes, which keep long-running work alive across calls.

Evaluation criteria for sandbox APIs

These are the dimensions that decide whether a runtime holds up in production. Whatever provider you favor, compare against the same checklist—the same discipline a Daytona alternatives evaluation forces.

Persistence (the deciding axis for agents). Does the environment survive between executions, and for how long? Can you snapshot, pause, and resume? Most generic comparisons treat this as a feature row; for multi-step agents it's the primary criterion.

Isolation model. Plain containers share the host kernel—a kernel-level exploit can cross the boundary. Stronger options run a user-space kernel (gVisor) or a microVM (Firecracker, Kata). gVisor intercepts syscalls in user space, putting a real kernel boundary between the workload and the host; microVMs give each workload a dedicated kernel. For untrusted agent-generated code in production, plain container-only isolation is generally not enough—gVisor or a microVM is the baseline.

GPU acceleration. Can the sandbox run GPU workloads in the same environment? Agents that execute inference, fine-tuning, or data-heavy computation need this, and most general-purpose sandboxes don't offer it—E2B, the most popular agent SDK, has no GPU at all. This is a core Beam differentiator.

Cold start latency. Time from request to ready environment. Sub-second feels interactive; multi-second is noticeable in a loop that runs dozens of times. Just as important is whether you pay for it: some providers bill the spin-up and image-pull window, Beam does not.

Session duration. How long a single sandbox can stay alive. Long-horizon tasks run for many minutes or hours; low hard caps (E2B's 24 hours, for instance) force awkward checkpointing.

BYOC and bring-your-own-compute. Can the runtime execute inside your own cloud account, so sensitive code and data never leave your infrastructure? Often a hard requirement for regulated teams. Beam supports bringing your own compute from AWS, GCP, Azure, and Hetzner, plus a fully self-hosted option.

Best code execution sandboxes for AI agents compared

Beam leads on the combination this guide centers: gVisor isolation, GPU acceleration, persistent snapshots, and bring-your-own-compute in one runtime—plus a billing model that doesn't charge for cold-start or image-pull time, which matters most precisely for the high-churn, many-spin-up pattern agents create. It's aimed at production agents where compute intensity and state continuity are both non-negotiable.

Modal excels at serverless GPU execution and batch workloads, but it's managed-only with no BYOC, and its sandbox compute bills at roughly 3× its standard function rate—easy to under-budget.

E2B pairs strong Firecracker microVM isolation with a clean code-interpreter API, a popular general-purpose pick—but it offers no GPU and caps Pro sessions at 24 hours. If you're weighing it specifically, see our E2B alternatives breakdown.

Daytona optimizes for cold-start speed (sub-90 ms) but defaults to container-level isolation sharing the host kernel, with Kata microVMs only optional. Northflank is a full platform spanning sandboxes, databases, APIs, and CI/CD with the cheapest published CPU rate—strong when you need far more than code execution, and the most direct competitor on BYOC and session length.

No runtime wins every case. Match isolation, persistence, and compute to your workload rather than to a headline.

How the providers compare on cost

A runtime that's cheap per-second can still be expensive at agent scale, where hundreds of sandboxes spin up and tear down constantly. Two things matter as much as the per-unit rate: whether you pay for idle and cold-start time, and whether GPU is billed all-inclusive or stacked on top of CPU and RAM.

Two cost dynamics are easy to miss and worth calling out for agent workloads specifically. First, idle and cold-start billing. E2B bills the full wall-clock time a sandbox is alive regardless of CPU activity, and Modal reclaims idle containers aggressively, which raises cold-start frequency. Beam doesn't charge for cold-start or image-pull time at all—an advantage that grows with how often your agents spin sandboxes up. Second, GPU billing structure. Northflank bundles CPU and RAM into one all-inclusive GPU rate; Beam, Modal, and Daytona bill GPU separately, so compare total cost for your actual configuration, not just the headline GPU number.

See Beam pricing for current rates.

Language runtime support: Java, Golang, PHP, and beyond

A sandbox should run the languages your agents actually generate. Because it's a full OS environment rather than a single-language interpreter, broad support is the norm—the agent can install runtimes and dependencies on demand through the package manager.

Beam sandboxes run non-root containers and support running the full Docker daemon, so an agent can install arbitrary runtimes and dependencies on demand rather than being limited to a fixed language set.

For agents written in TypeScript, Beam's TypeScript SDK reference covers programmatic sandbox control directly from agent code.

Build stateful AI agents with Beam

If your agents do real work (multi-step coding, long-horizon workflows, GPU-backed computation) the decision comes down to persistence, isolation, and compute. Beam combines stateful snapshots, gVisor isolation, GPU acceleration, and bring-your-own-compute so agents operate against a continuous, secure, production-grade workspace instead of starting from zero on every action—without paying for cold-start or image-pull time along the way.

The agentic apps guide shows how persistent sandboxes fit into a full agent system. When you're ready to compare cost against your workload, see Beam pricing.

Frequently asked questions

What is a code execution sandbox and how does it work? A code execution sandbox is an isolated environment that runs untrusted or AI-generated code without giving it access to the host system. It provides a real filesystem, shell, and network stack inside a security boundary—typically enforced by a user-space kernel like gVisor or a microVM—so code runs freely while the host and other workloads stay protected.

What is the difference between remote code execution and arbitrary code execution? Arbitrary code execution (ACE) is the capability to run any code on a system. Remote code execution (RCE) is that same capability triggered remotely by an attacker over a network—a security vulnerability. A sandbox is what makes intentional arbitrary code execution safe: you deliberately run untrusted code, but the isolation boundary stops it from compromising the host.

Should a code execution sandbox be stateful or ephemeral for AI agents? For multi-step agents, stateful. Long-horizon tasks—editing code, installing dependencies, iterating on test results—depend on state carrying forward, and an ephemeral runtime forces the agent to repeat setup on every step. Ephemeral runtimes still fit genuinely one-shot tasks where each execution is independent.

Is container-based isolation enough for a code execution sandbox, or do you need a microVM? Plain containers share the host kernel, so a kernel-level exploit can break out—a real risk with code you don't control. Stronger isolation comes from a user-space kernel (gVisor), which intercepts syscalls in user space, or a microVM (Firecracker, Kata), which gives each workload a dedicated kernel. For untrusted agent-generated code in production, plain container-only isolation generally isn't sufficient; gVisor or a microVM is the baseline.

What languages does a sandboxed runtime support, including Java, Go, and PHP? Because a sandbox is a full operating-system environment rather than a single-language interpreter, it can run essentially any language—Python, JavaScript/TypeScript, Java, Go, PHP, Ruby, Rust, and more. The agent installs runtimes and dependencies through the sandbox's package manager as needed, so support isn't limited to a fixed list.

How does a sandbox API enable AI agents to execute code safely at scale? A sandbox API lets an agent programmatically create isolated environments, run commands, stream output, and manage files with no human in the loop. Each agent or task gets its own boundary, so untrusted code is contained per-session, and the API handles provisioning and teardown—letting many agents execute code concurrently while isolation keeps them from affecting each other or the host.

How do leading code execution sandbox providers compare in 2026? They differ mostly in focus. Beam centers on stateful, GPU-capable production agent runtimes with gVisor isolation and bring-your-own-compute, and doesn't bill for cold-start or image-pull time; Modal excels at serverless GPU execution but is managed-only and prices sandboxes at roughly 3× its function rate; E2B offers strong Firecracker microVM isolation with a clean SDK but no GPU and a 24-hour session cap; Daytona has the fastest cold starts (sub-90 ms) but defaults to container-level isolation; and Northflank is a complete platform with microVM isolation, unlimited sessions, and the cheapest published CPU rate. Choose based on your persistence, isolation, and compute requirements rather than a single ranking.