E2B

✓ Editorially verified

Secure cloud sandboxes for running AI-generated code

Freemium· Hobby: Free with $100 one-time credits, up to 1-hour sessions, 20 concurrent sandboxes. Pro: $150/mo + usage, 24-hour sessions, 100 concurrent sandboxes (extendable to 1,100). Ultimate: custom enterprise pricing. Usage billed per second: vCPU $0.000014-$0.000112/s, RAM $0.0000045/GiB/s.AgentsLLM-agnostic (works with GPT-4o, Claude, Mistral, Llama, and self-hosted models)

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Best for

AI engineering teams shipping code-interpreter features, autonomous coding agents, or data-analysis agents that need to execute untrusted LLM-generated code with real filesystem and network access.

Skip if

Solo hobbyists who only need occasional Python execution (the OpenAI Assistants code interpreter is cheaper), or teams looking for a full agent framework rather than a sandbox runtime.

E2B is a cloud infrastructure platform that gives AI agents secure, isolated sandboxes to execute code, run shell commands, browse the web, and interact with files, all inside Firecracker microVMs that spin up in under 200ms. It's the runtime layer most teams reach for when they need to safely execute LLM-generated Python, JavaScript, or arbitrary shell code without exposing their own servers to prompt-injected commands or runaway processes.

The platform ships Python and Node.js SDKs plus prebuilt integrations with LangChain, LangGraph, LlamaIndex, and the major LLM providers (OpenAI, Anthropic, Mistral, Llama, and self-hosted models). A typical workflow: your agent generates code, E2B boots a fresh microVM, executes the code with a full filesystem and network, streams stdout/stderr/artifacts back, and tears the VM down. Sessions can run for up to 24 hours on Pro, and each sandbox supports pip/npm installs, custom Docker-style templates, a persistent filesystem, a browser (via the Desktop template), and terminal access.

E2B is used by 94% of the Fortune 100 according to the company, and the core sandbox runtime is open-source with 12k+ GitHub stars, so teams that need airgapped or on-premise deployments can self-host the same primitives. Common use cases include code-interpreter features inside chat products, data-analysis agents that need pandas/matplotlib, autonomous coding agents (SWE-agent style), computer-use / browser-use agents, and general tool-execution backends for multi-agent systems. Deployment options span managed cloud, BYOC on your own AWS/GCP account, and fully self-hosted.

Editor's take

E2B is the default answer when I need to run LLM-generated code in production without terrifying my security team. The Firecracker isolation is real, the SDK is boring in the best way, and self-hosting is a genuine escape hatch, not a checkbox. My main gripe is the pricing math: the $150/mo Pro floor plus per-second usage adds up fast once you scale past prototypes, so model the bill before you commit.

— The AI Tool Bible editorial team

Pros

✅ Sub-200ms sandbox startup with no cold starts, thanks to Firecracker microVMs
✅ True VM-level isolation, safe for arbitrary LLM-generated code and untrusted shell commands
✅ Long-running sessions up to 24 hours, enough for multi-step data or coding agents
✅ Open-source core with self-host and BYOC options for airgapped or regulated environments
✅ LLM-agnostic with first-party support in LangChain, LangGraph, LlamaIndex, and OpenAI/Anthropic SDKs
✅ Custom sandbox templates let teams pre-bake dependencies and skip package-install latency
✅ Generous $100 free-credit tier makes prototyping essentially free

Cons

⚠️ Per-second usage billing on top of the $150/mo Pro fee can get expensive for high-volume agent traffic
⚠️ Concurrency cap of 100 sandboxes on Pro (extendable at extra cost) can bottleneck production apps
⚠️ Self-hosting requires operating Firecracker, which is nontrivial versus just calling an API
⚠️ Free Hobby tier is a one-time credit grant, not an ongoing free allowance
⚠️ Focused purely on execution infrastructure, no built-in agent framework, planner, or memory layer

Use cases

Code interpreter for chat appsAutonomous coding agentsData analysis with pandas and matplotlibBrowser-use and computer-use agentsLLM tool execution backendSWE-agent style repo editsSecure sandbox for untrusted user codeMulti-agent workflow runtimeAI-generated report generationRAG pipelines with code execution steps