Sandboxing AI-Generated Code: E2B vs Modal vs Daytona vs Vercel Sandbox
Where should agent-written code run? The four sandbox platforms compared — isolation models, persistence, economics — plus the design rules that keep execution safe.
Agent-written code needs somewhere safe to run, and four platforms own the category: E2B (the code-interpreter specialist with open Apache-2.0 infra and desktop VMs), Daytona (sub-90ms startup, multi-OS, AGPL self-host), Modal (sandboxes inside a full serverless GPU platform), and Vercel Sandbox (Firecracker microVMs native to the Vercel ecosystem).
Key takeaways
- The principle is non-negotiable: code a model wrote runs in isolation — its own VM, filesystem, and network — never in your application's process or host.
- All four leaders converged on microVM-grade isolation (Firecracker or equivalent); differentiation moved to startup latency, persistence, OS breadth, and ecosystem fit.
- E2B is the agent-native specialist (code-interpreter SDKs, pause/resume, desktop sandboxes); Daytona competes on speed and Windows/Android; Modal bundles sandboxes with serverless GPUs; Vercel Sandbox wins inside Vercel stacks.
- Persistence is the sleeper feature: stateful sandboxes (snapshots, resume-by-name) turn one-shot code execution into long-running agent workspaces.
- Sandbox ≠ policy: you still scope network egress, secrets, and resource/time limits — isolation contains the blast, configuration decides its radius.
The moment agents could write code, the question became where it runs — because generated code is untrusted input that executes. The answer that won: give every agent a disposable computer. Four platforms industrialized that answer, and they're more alike on safety than their marketing suggests — which moves the real decision elsewhere.
The short list
| Platform | Pick it for | Posture |
|---|---|---|
| E2B | Agent-native code interpreters, desktop VMs | The specialist (Apache-2.0 infra) |
| Daytona | Sub-90ms starts, Windows/Android, self-host | The speed & breadth play (AGPL) |
| Modal | Sandboxes + serverless GPUs in one platform | The compute platform |
| Vercel Sandbox | Vercel-ecosystem products | The native integration |
What actually differs
Isolation: table stakes. Firecracker microVMs (E2B, Vercel) or equivalent kernel-level isolation (Daytona's dedicated kernels, Modal's secure containers) — own filesystem, own network, no path to your environment. Nobody competitive ships less; stop comparing here.
Ergonomics and ecosystem: the real fight. E2B's SDKs speak agent natively (run_code with rich outputs, charts included) and its production infra is open source — plus Desktop Sandboxes for computer-use agents. Daytona leads on spin-up latency (sub-90ms claimed) and is alone on Windows/Android sandboxes. Modal's sandboxes live inside a serverless platform — one vendor for execution and GPU inference and batch. Vercel Sandbox inherits your Vercel project's auth, billing, and idioms — if v0 and the AI SDK are your stack, it's right there.
Persistence: the sleeper. All four now do stateful sandboxes — E2B's pause/resume, Daytona's snapshots and volumes, Vercel's persistent-by-default snapshots, Modal's reattach-by-ID — which upgrades the category from "run this snippet" to long-running agent workspaces that survive between sessions.
Economics: shapes, not prices. Per-second metering everywhere, with different free tiers (E2B and Daytona credits, Vercel's monthly Hobby allotment, Modal's monthly plan credits) and different self-host outs (E2B Apache-2.0, Daytona AGPL). Idle-but-running sandboxes bill on most meters — lifecycle hygiene is a cost control.
The rules that keep it safe
A sandbox contains the blast; configuration decides the radius. (1) Egress is policy — default-deny or allowlist network from the sandbox; exfiltration is the attack that isolation alone doesn't stop. (2) No ambient secrets — inject the single scoped credential a task needs, never your env. (3) Budget everything — timeouts, CPU/memory caps, and step limits turn runaway generation into a bounded bill. (4) Treat outputs as untrusted too — results feed back into the model; injection can ride return values. (5) Log execution — what ran, what it touched: your audit trail when something weird happens. This is guardrails discipline applied to the execution layer — and item one on the agentic security checklist.
Frequently asked questions
- Why can't the agent just run code on my server?
- Because generated code is untrusted by definition — not necessarily malicious, but unvetted: it can exfiltrate env vars, eat resources, or follow injected instructions from content the model read. A sandbox gives it a real computer with nothing of yours inside: own filesystem, own network policy, disposable by design. This is the execution-layer version of least privilege.
- Which sandbox platform should I pick?
- By gravity: building agent products from scratch → E2B (the SDKs and patterns are agent-native); needing fastest spin-up or Windows/Android targets → Daytona; already running inference/batch on Modal → its Sandboxes keep one platform; deployed on Vercel → Vercel Sandbox shares your auth, billing, and SDK idioms. Isolation quality won't be your differentiator — fit will.
- Do I need a sandbox for code the agent writes into my repo?
- Different threat: repo code gets reviewed and tested before it runs with privileges (that's the verification stack). Sandboxes are for code that executes IMMEDIATELY as part of the agent loop — analysis snippets, generated scripts, tool-built programs — where review-before-run is impossible by design.
Related
- E2bOpen-source Firecracker-microVM sandboxes where AI agents safely execute untrusted code — stateful code interpreters with full Linux, pause/resume, and desktop VMs.
- DaytonaSub-90ms agent sandboxes — isolated computers with snapshots, volumes, Git and LSP tools, on Linux, Windows, or Android; AGPL self-host or managed cloud.
- ModalServerless AI infrastructure in pure Python — GPU functions with sub-second cold starts, secure sandboxes for agent code, batch jobs, and per-second billing.
- Vercel SandboxEphemeral Firecracker microVMs on Vercel for untrusted and AI-generated code — millisecond startup, Node and Python runtimes, persistent by default.
- How Computer-Use Agents WorkInside the perception-action loop that lets AI operate real software — screenshots in, clicks out — plus grounding, reliability, and when to use APIs instead.
- GuardrailsGuardrails are programmatic checks around an LLM — validating inputs and outputs in code — enforcing safety and format rules a prompt alone can't guarantee.
- Agent EngineeringAgent engineering is the discipline of building reliable AI agents — designing the tools, context, guardrails, evals, and recovery paths around the model.
- Securing AI Agents: The OWASP Agentic Top 10 in PracticeAgents add risks LLM-app security misses — autonomy, tools, memory, multi-agent trust. The key OWASP agentic threats and how to mitigate each in practice.