E2B

What is E2B?

E2B is a cloud platform that gives every AI agent session its own isolated Linux machine for running code, commands, browsing and tools away from your servers. You supply the model, prompts and agent logic, and E2B supplies the computer.

Each sandbox is a Firecracker-based microVM with its own kernel, started from a snapshot so it boots at close to container speed. Developers create and control sandboxes through SDKs for Python and JavaScript or TypeScript, using an API key. Sandboxes can be paused, resumed, snapshotted and forked, and the docs say a paused sandbox keeps both its files and memory.

The legal entity named on the site is FoundryLabs, Inc., trading as E2B. Its core runtime is published on GitHub under the Apache-2.0 licence. The homepage claims more than 100,000 teams, over one billion sandboxes started and sign-ups from 94 of the Fortune 100, figures we could not check independently.

Key Features

Per-session microVMs matter most in E2B, because each agent gets a full Linux machine with its own kernel instead of sharing a container with other workloads.

• Isolated microVM sandboxes: Every session runs in a dedicated Firecracker microVM with its own kernel. Untrusted, AI-written code is therefore kept well away from your own infrastructure and from other customers’ agents and data.
• Code interpreter SDK: A dedicated code-interpreter package runs Python and JavaScript and returns results to your agent. It suits data analysis assistants that need to execute generated scripts and read back charts or output.
• Pause, resume and fork: Sandboxes can be paused and later resumed with filesystem and memory intact, or snapshotted and forked. Long-running agents can therefore pick up where they left off rather than rebuilding state.
• Desktop sandboxes: An E2B Desktop SDK provides a full Linux desktop with the XFCE environment. Computer-use agents can see the screen, click and type inside the sandbox rather than on a real machine.
• Networking and secrets: Sandboxes can expose public URLs and follow custom ingress and egress rules. A secrets vault lets agents use API keys through an egress proxy without being able to read the keys themselves.
• Custom templates: Templates define what a sandbox contains when it starts, such as packages and files. Ready templates are listed for Claude Code, the OpenAI Agents SDK, CrewAI, Agno and Mastra.

Pros

Strong isolation with an open-source core is E2B’s main appeal, since teams get microVM-level separation and can inspect the Apache-2.0 runtime themselves.

• Security by design: Running each session in its own microVM limits what malicious or buggy generated code can reach. The site also lists SOC 2 Type II and HIPAA compliance for its managed cloud.
• Generous free start: The Hobby plan needs no credit card and includes a one-time $100 usage credit. Developers can prototype agents for weeks before deciding whether to pay for Pro.
• Developer-friendly SDKs: Python and JavaScript packages install with a single command and need only an E2B_API_KEY variable. Creating a sandbox and running a command takes a few lines of code.
• Flexible deployment: Teams can use the managed cloud in US, EU and APAC regions or a bring-your-own-cloud option on AWS, GCP or Azure. Sensitive workloads can then stay inside the customer’s own network.
• Proven at scale: Named users include Hugging Face, Meta, Groq, Manus and Gumloop. Enterprise plans support tens of thousands of concurrent sandboxes, which suits products serving many agents at once.

Cons

Usage-based billing is the catch with E2B, because costs grow with every second of vCPU and RAM your sandboxes consume on top of the Pro fee.

• Costs can be hard to predict: Usage is billed per second for vCPU and RAM, plus the $150 Pro fee. Busy agents left running can build up charges, so set timeouts and monitor usage dashboards.
• Short sessions on Hobby: Free sandboxes last up to one hour, with a cap of 20 running at the same time. Longer-running agents need Pro for 24-hour sessions or Enterprise for multi-day persistence.
• Steep Enterprise entry: Enterprise starts at a $3,000 monthly minimum, according to the pricing page. Smaller firms needing BYOC or very high concurrency may find that threshold hard to justify.
• Developer skills required: E2B is infrastructure, not a finished app, and it has no visual builder. Non-developers need an engineer to wire it into an agent framework before seeing any value.
• Self-hosting has limits: The GitHub README lists Terraform self-hosting on AWS and Google Cloud only, with Azure and generic Linux unsupported. That conflicts with the broader BYOC list on the homepage, so confirm your platform first.

Who is Using E2B?

AI product teams building agents that write and run code are E2B’s main users, from start-ups to large technology companies named on its site.

• AI start-ups: Companies such as Manus, Lindy and Genspark are named on the homepage. They give their agents a safe computer for tasks like research, coding and automation.
• Model and research labs: Organisations such as Hugging Face and LMArena appear among its customers. They use sandboxes to run and evaluate model-generated code at scale.
• Data and analytics products: Builders of data assistants use the code interpreter to execute analysis scripts. Results such as tables and charts flow back to the user safely.
• Enterprise platform teams: Large firms use BYOC or embedded deployments to keep agent workloads inside their own cloud accounts. This helps meet internal security and data residency rules.

Pricing

E2B charges a monthly plan fee plus per-second compute, with a free Hobby tier carrying a one-time $100 credit (official pricing page, October 2026).

Futurepedia lists E2B as freemium with a $150 tier, which matches the official page, though it does not show the usage rates.

Disclaimer: per-second rates add up differently for every workload, so model your expected sandbox hours against the official calculator figures.

What Makes E2B Unique?

Firecracker microVMs paired with an open-source runtime set E2B apart from closed sandbox services. You can read the Apache-2.0 code, run the stack yourself and still use a managed cloud when that is easier.

Stateful lifecycle controls are the second difference. Pausing a sandbox keeps memory and files, and forking lets an agent branch from one state into several attempts. Many code-execution tools only offer short, disposable runs, which suits single scripts but not long agent tasks.

Compatibilities and Integrations

Python and JavaScript SDKs are the main way in, with ready templates for popular agent frameworks and deployment options across the three big clouds.

• SDKs: The e2b, code-interpreter and desktop packages are published on npm and PyPI. The docs name E2B_API_KEY as the environment variable for authentication.
• Agent frameworks: Templates are listed for Claude Code, OpenAI Agents SDK, CrewAI, Agno and Mastra. You can also build your own template with custom packages.
• Cloud deployment: The managed cloud runs in US, EU and APAC regions. BYOC places sandboxes in your AWS, GCP or Azure account, and E2B Embed supports Docker Compose, Terraform or Kubernetes.
• Storage and data: Sandboxes have a persistent filesystem and can connect to cloud buckets such as Amazon S3. Files can be uploaded and downloaded through the SDK.
• Observability: Metrics, logs and signed webhooks report sandbox activity. The docs also offer an MCP endpoint so AI assistants can query the documentation.

E2B Tutorials

Six steps get a first sandbox running from a Python or JavaScript project.

1. Sign up at e2b.dev on the free Hobby plan and copy your API key from the dashboard.
2. Set the key as the E2B_API_KEY environment variable in your project.
3. Install the SDK with pip install e2b or npm i e2b, or the code-interpreter package for running scripts.
4. Create a sandbox in code, run a shell command and print the output.
5. Connect the sandbox to your agent so model-generated code runs inside it rather than locally.
6. Set timeouts, pause idle sandboxes and watch usage in the dashboard before moving to Pro.

How We Rated It

Isolation and developer tooling lift E2B to 4.3 out of 5, while unpredictable usage costs and the high Enterprise minimum hold it back.

Summary

E2B suits developers and AI teams who need agents to run untrusted code, use a desktop or browse inside a properly isolated machine. The main limitation is cost control, since per-second billing on top of the Pro fee can climb quickly with long-running agents. Its open-source runtime, compliance claims and BYOC option make it a credible choice for larger firms too. We rate it highly for security and features but lower for cost predictability. Start on the free credit, measure real sandbox hours, and set strict timeouts before you scale.