How to Run AI-Generated Code Safely with E2B Sandboxes

E2B gives you throwaway cloud sandboxes where AI-generated code can run without touching your own machine or users. If your app lets an LLM write and execute Python or JavaScript, E2B is the firebreak between that code and your infrastructure.

Direct answer: E2B runs each code execution in an isolated Firecracker microVM that boots in about 150ms; you call it through the Python or JS SDK, and the sandbox is destroyed automatically so nothing leaks between runs.

Why not just use a Docker container?

Containers share a kernel, so a determined escape or a fork bomb can still hurt the host. E2B uses Firecracker microVMs with their own kernel, which is a stronger boundary. It also starts faster than a cold container pull and gives each run a clean filesystem by default — no state carried over from the last execution.

How do I get started?

1. Sign up at e2b.dev and copy your API key from the dashboard.
2. Install the SDK: pip install e2b-code-interpreter.
3. Export the key: export E2B_API_KEY=your_key.
4. Run a snippet:

from e2b_code_interpreter import Sandbox
with Sandbox.create() as sbx:
    r = sbx.run_code("import pandas as pd\nprint(pd.__version__)")
    print(r.logs.stdout)

That’s it. The sandbox spins up, runs your code, and tears down when the block exits.

Can I install packages and upload files?

Yes. Call sbx.commands.run("pip install requests") before your code, and push files with sbx.files.write("/data.csv", content). Files are scoped to that one sandbox, so a fresh run starts empty unless you attach a persistent volume.

How much does E2B cost?

The free tier includes $100 of compute credit for new accounts. Paid usage is metered by sandbox-seconds: roughly $0.000014 per second for the 1-vCPU tier, so a 2-minute session is a fraction of a cent. RAM is the bigger lever — a 2GB sandbox costs about double the base rate.

What’s it not good for?

Long-running jobs are a poor fit; sandboxes are meant to be short-lived and the default timeout is 5 minutes, extendable but not indefinitely. There’s no GPU on standard sandboxes, so ML training or heavy inference is off the table. And it’s a US-hosted cloud — if data residency rules say EU-only, you’ll need a different approach.

Who should use E2B?

Teams building code-interpreter features, data-analysis agents, or any product where an LLM writes code that must run. If you only execute your own code, a plain container is simpler and cheaper.

Key Takeaways

• E2B isolates each execution in a Firecracker microVM that boots in ~150ms.
• Default sandbox timeout is 5 minutes and can be extended.
• Free tier gives $100 in compute credit; usage meters at about $0.000014 per sandbox-second.
• No GPU on standard sandboxes — not for training or heavy inference.