Your agent needs to run code, and the hosted options don't give you enough control. OpenAI's Code Interpreter is a black box — you can't bring your own dependencies, you can't touch the runtime, and you definitely can't get a GPU. So you're looking for a standalone code execution API you can wire into a tool call: spin up a sandbox, run the model's output, get the result back, tear it down. This guide compares the five platforms worth shortlisting — Beam, E2B, Modal, CodeSandbox, and Daytona — on the things that actually decide the build: isolation model, deployment flexibility, cold-start latency, GPU access, and price.

The five contenders at a glance

A note on isolation, since it's the column people misread: E2B and CodeSandbox run Firecracker microVMs, which give each sandbox its own kernel — the strongest boundary in this table for adversarial multi-tenant code. Beam and Modal use gVisor, a userspace kernel that intercepts syscalls to shrink the host attack surface. gVisor is strong, production-grade isolation (it's what Google runs Cloud Run on); it trades a thin slice of the microVM's hardware-level separation for dramatically faster custom-image starts. For most agent workloads — your code, your dependencies, running your model's output — gVisor is the right point on the curve. If you're executing genuinely hostile third-party code in a shared multi-tenant pool, weigh Firecracker.

Beam (beam.cloud) — the most control over how and where your code runs

What it is. Beam is an open-source serverless platform for AI workloads, and its Sandbox API is the relevant primitive for agents. What separates it from the rest of this list isn't the SDK — it's the deployment model. Beam's runtime, beta9, is open source (AGPL-3.0), so you're not locked into a single managed cloud. You can run Beam's infrastructure on your own AWS, GCP, or Azure account — using credits you already have — or connect your own hardware and run the same sandbox API on any VM, which means you can source the cheapest GPUs available and let Beam orchestrate them. For most teams that need to run AI-generated code without handing control of the runtime, the cloud, or the cost structure to a vendor, that combination makes Beam the default pick.

Isolation. Beam builds containers using runc and gVisor. The gVisor runtime is also why Beam launches sandboxes with custom dependencies in under one second, where Firecracker-based platforms can be slower to mount and boot a large custom image. Beam is SOC 2 compliant.

Integration pattern (Python) — from docs.beam.cloud:

For a GPU sandbox, declare resources at creation: Sandbox(cpu=4, memory="16Gi", gpu="A10G"). You also get sb.process.exec("ls", "-la") for arbitrary shell, sb.fs.upload_file() / sb.fs.download_file() for moving data in and out, sb.expose_port(8000) for live preview URLs, and sb.update_ttl(seconds) to extend the sandbox lifetime mid-run.

Cold start, timeout, secrets. Sandboxes cold-boot in 1–3 seconds (under 1 s for cached custom images). Lifetime is controlled by keep_warm_seconds at creation (-1 keeps it alive until you call terminate() manually); sb.update_ttl(300) resets the clock if a long tool call is still running. Secrets are passed inline via Sandbox(env={...}) or attached from Beam's secret manager and exposed as environment variables inside the sandbox.

Pricing. CPU $0.0000528/core/s ($0.190/hr), RAM $0.0000056/GB/s ($0.020/hr), RTX 4090 $0.69/hr, A10G $1.05/hr, H100 $3.50/hr — the cheapest managed H100 in this comparison. Sandboxes scale to zero by default, and you aren't billed during image pulls or queue waits. Signup includes free credits ($30/month, refreshed monthly). And because the core is open source with BYOC, the managed price is a ceiling, not a floor — run it on your own cloud or hardware and the economics are yours.

Adjacent products. Airstore is Beam's open-source virtual filesystem for AI agents (turn Gmail/GitHub/Linear into files an agent can read). Capsule (docs.capsule.new) is a separate product for hosting full agentic apps end-to-end — neither is required to use Beam Sandboxes.

Honest tradeoff. gVisor, not Firecracker — if your threat model is hostile third-party code in a shared multi-tenant pool, evaluate the microVM options below. And the open-source/BYOC path that makes Beam compelling does mean operating infrastructure if you go self-hosted; the managed cloud exists precisely so you don't have to, but the choice is yours to make.

E2B (e2b.dev) — Firecracker microVMs, drop-in Code Interpreter

What it is. E2B runs each sandbox as a Firecracker microVM with a dedicated kernel — the strongest isolation boundary in this guide, and the same primitive AWS built for Lambda and Fargate. It's the most explicitly agent-focused product here, with documented integrations for every major framework.

Integration pattern (Python):

The e2b_code_interpreter.Sandbox wraps a Jupyter kernel and persists state across run_code() calls. For arbitrary shell, use the base e2b.Sandbox and sandbox.commands.run('ls').

Cold start, timeout, secrets. Boots in ~150 ms. Default `timeout` is 300 s; extend via timeout= at creation or sandbox.set_timeout(seconds). Max session is 3,600 s (Hobby) / 86,400 s = 24 h (Pro). Secrets via Sandbox.create(envs={"API_KEY": "..."}).

Pricing (verified on e2b.dev/pricing). Hobby is free with $100 one-time credits, up to 20 concurrent sandboxes, 1-hour max sessions. Pro is $150/mo + usage (24-hour sessions, 100 concurrent). Compute metered per second: 2 vCPU default ≈ $0.10/hr. No GPUs.

Honest tradeoff. No GPUs. 24-hour hard session cap. Custom environments need Docker template builds. Self-hosting (e2b-dev/infra) is real but not turnkey.

Modal (modal.com) — sandboxes that can hold an exotic GPU

What it is. A Python-first serverless platform; sandboxes are one product among Functions, Notebooks, and Batch. Isolation is gVisor. The reason to reach for Modal over Beam specifically is GPU breadth — Modal is the only platform here that puts a B200 or H200 inside a sandbox.

Integration pattern (Python):

Cold start, timeout, secrets. Sub-second cold starts. Default sandbox lifetime 5 min, configurable to 24 h via timeout=; idle_timeout= auto-terminates idle sandboxes. Secrets are first-class modal.Secret objects.

Pricing (verified on modal.com/pricing). Starter free ($30/mo credit); Team $250/mo. GPU per-second: B200 $0.001736, H200 $0.001261, H100 $0.001097 (~$3.95/hr), A100-80 $0.000694, L4 $0.000222, T4 $0.000164. Sandbox CPU bills on the non-preemptible tier at $0.00003942/core/s — 3× the standard Function rate, easy to miss.

Honest tradeoff. Sandbox compute is meaningfully pricier than standard Modal Functions. No BYOC or on-prem. Overkill and overpriced versus Beam, E2B, or Daytona for pure CPU work.

CodeSandbox & Daytona — the specialists

CodeSandbox (acquired by Together AI, Dec 2024) runs full Linux VMs on Firecracker (~2 s boot) and exposes them via a TypeScript SDK with fork, hibernate, and resume semantics — uniquely useful for running parallel agent branches off a single base state.

Free tier 400 credits/mo; Pro $9–12/mo + on-demand $0.18/hr per VM. No GPUs, TS-primary SDK. Reach for it only when forking and memory snapshots are core to your product — otherwise E2B is faster to integrate.

Daytona (daytona.io) is container-based (Docker default, Kata/Sysbox optional) with sub-90 ms warm-pool starts, persistent filesystems, and pause/archive lifecycles built for sandboxes that survive across sessions.

Pricing: $0.0504/vCPU-hr + $0.0162/GiB-hr; H100 $3.95/hr, $200 free credit. Default auto-stop is 15 min of inactivity (auto_stop_interval=0 disables). Honest tradeoff: Docker-default isolation is weaker than Firecracker unless you opt into Kata, and there are fewer agent-specific examples than E2B.

Decision guide

• Choose Beam if you want control over the runtime, the cloud, and the cost — open-source core, no lock-in, run on your own AWS/GCP/Azure credits or your own hardware, gVisor isolation, and the cheapest managed H100 ($3.50/hr). This is the right default for most teams running AI-generated code.
• Choose E2B if you specifically want Firecracker microVM isolation for hostile multi-tenant code, and the fastest path to a stateful Code Interpreter tool call.
• Choose Modal if your agent needs a GPU more exotic than an H100 (B200/H200), or you already run other Modal Functions.
• Choose CodeSandbox if your agent forks decision branches and benefits from memory snapshots and a tight web-preview loop.
• Choose Daytona if the sandbox must persist across sessions and you want first-party multi-language SDKs.

FAQ

How do I plug one of these into my agent framework? All five expose a run_code()-style call you wrap in your framework's tool abstraction — a few lines in any of them. The cleaner architecture is to keep the agent on your own server and treat the sandbox purely as an execution tool it calls remotely; that way your secrets and agent state stay in your app context rather than living inside the sandbox.

What happens if the code runs too long? Every platform has a default timeout and a way to extend it. Beam uses keep_warm_seconds at creation and sb.update_ttl(seconds) to extend mid-run (set keep_warm_seconds=-1 to keep alive until you terminate manually). E2B defaults to 300 s, extended via set_timeout(), capped at 24 h on Pro. Modal defaults to 5 min, configurable to 24 h. Daytona auto-stops after 15 min idle. For workflows longer than the hard caps (E2B, Modal), checkpoint state and resume into a fresh sandbox.

Is GPU access available? Beam (RTX 4090, A10G, H100), Modal (T4 through B200), and Daytona (H100, RTX PRO 6000) support GPUs in the sandbox. E2B and CodeSandbox do not — their Firecracker microVMs don't do GPU passthrough.

How do I handle secrets / env vars inside the sandbox? All five inject secrets as environment variables at creation rather than baking them into the image. Beam: Sandbox(env={...}) or its secret manager. E2B: Sandbox.create(envs={...}). Modal: first-class modal.Secret objects. Daytona: envVars={...}. Keep secrets in the create call, not in code the model can echo back.