E2B

What is E2B?

E2B is infrastructure for one very specific, very important job, giving AI agents a safe place to run code. If you are building an agent that writes Python, opens files, installs packages, browses the web, or manipulates a desktop, that code has to execute somewhere. E2B’s answer is an isolated cloud sandbox, built on Firecracker microVMs, that starts in under 200 milliseconds and keeps untrusted code away from your app servers and other users.

The company was founded in 2023 by Vasek Mlejnsky and Tomas Valenta, and is based in San Francisco. We found a company that has moved unusually fast for an infrastructure startup. E2B says 88% of Fortune 100 companies have signed up or trialed the platform, and the company has raised $32 million total, including a $21 million Series A led by Insight Partners. That traction matters because E2B is not selling a vague AI platform. It is solving a concrete production problem that appears the moment an LLM starts generating executable code.

What makes E2B interesting is how focused it is. This is not a general cloud platform with AI features added later. It is built around agent workflows, code execution, persistent sessions, and now desktop environments for computer-use agents. The core is open source, while the managed cloud handles the operational burden most teams do not want. In practice, E2B is used by teams building code interpreters, data analysis tools, coding agents, browser and desktop automation, and internal systems that need to execute AI-written code without taking reckless security risks.

Key Features

• Firecracker sandboxes: E2B runs workloads inside Firecracker microVMs instead of ordinary shared-kernel containers. That matters because the security boundary is stronger, and startup time is still fast, under 200ms according to E2B, which is quick enough for interactive agent workflows.

• Multi-language execution: Out of the box, E2B supports Python, JavaScript/TypeScript, R, Java, and Bash. For teams building agents that need more than notebook-style Python, this broadens what the agent can actually do, from data analysis to shell automation to backend code tasks.

• Persistent sessions and snapshots: E2B can preserve filesystem state, running processes, and memory state between runs. This changes the shape of agent design, because you are not forced into stateless one-shot execution, and E2B says resume time is about 1 second, with pause time around 4 seconds per GB of RAM.

• Custom templates: Teams can create prebuilt sandbox templates with dependencies, environment variables, and services already installed. In real usage, this cuts out repetitive setup work, especially for heavy stacks like data science environments where pandas, NumPy, and notebooks would otherwise need to install every time.

• Desktop sandboxes: E2B offers Ubuntu desktop environments with VNC streaming plus APIs for screenshots, clicks, typing, and scrolling. This is the feature that moves E2B beyond code execution into computer-use agents, where a model can inspect a UI and act on it step by step.

• LLM-agnostic integrations: E2B works with OpenAI, Anthropic, Mistral, Groq, Hugging Face, Ollama, and others. We think this matters more than it sounds, because infrastructure choices tend to last longer than model choices, and E2B does not force a bet on one model vendor.

• Code Interpreter SDK: E2B packages common execution patterns into SDKs for Python and JavaScript. Instead of building your own session management, file handling, stdout capture, and execution flow, teams can drop in a layer designed for AI-generated code from the start.

• MCP and Docker tool access: Through its Docker partnership, E2B sandboxes can connect to 200+ MCP tools from providers like GitHub, Notion, Stripe, Perplexity, Browserbase, and ElevenLabs. That gives agents a larger toolset without each team needing to hand-roll every integration.

• Concurrency controls: The free tier supports up to 20 concurrent sandboxes, and Pro starts at 100, with add-ons up to 1,100. For small agent products this is enough to launch quickly, but for high-volume systems it also gives a rough sense of when you will need to talk to sales.

Use Cases

One of the clearest examples we found is Perplexity. E2B says Perplexity used it to power code analysis for paid users and got the feature live in one week. That is a good illustration of E2B’s appeal. The team did not need to build a secure code execution layer from scratch, they plugged into infrastructure that already handled isolation, lifecycle management, and runtime environments.

A second pattern shows up in data analysis apps. E2B’s Code Interpreter SDK is used to build ChatGPT-style “analyze this file” experiences where a user uploads a CSV, asks a question in plain English, and the model writes Python to inspect the data, generate charts, and return findings. In this setup, the sandbox is the product. Without it, the app either cannot run code at all or has to expose the company’s own servers to arbitrary generated scripts.

There is also a growing set of computer-use projects. E2B Surf, the company’s open-source reference agent, shows how a model can operate a full Ubuntu desktop by reading screenshots and sending clicks and keystrokes. That opens up work on browser automation, QA testing, legacy software interaction, and tasks where no clean API exists. It is still an emerging category, but E2B is one of the few tools that has turned it into a usable developer primitive.

On the enterprise side, E2B names customers and users including Groq, Hugging Face, LMArena, Vercel, and Commure. The stories vary, but the common thread is safe execution at scale. Commure’s healthcare context stands out because it points to why teams care about isolation beyond convenience. In sensitive environments, “just run the code in a container” is not a satisfying answer.

Strengths and Weaknesses

Strengths:

• It solves a real production problem, not a demo problem. A lot of AI tooling looks impressive until you ask where the generated code actually runs. E2B has a direct answer, and that is why companies like Perplexity and Groq show up in its customer list.

• The security story is stronger than container-first alternatives. Compared with platforms built mainly around Docker containers, E2B’s Firecracker approach gives teams a more convincing isolation boundary for untrusted code. If you are evaluating E2B against Daytona, this is probably the biggest architectural difference.

• Fast enough for interactive products. “Secure sandbox” often implies slow startup and clunky UX. E2B’s sub-200ms startup claim matters because agent products live or die on responsiveness, especially when execution sits in the middle of a user conversation.

• Open-source core builds trust. We saw repeated evidence that E2B’s open-source model helped it spread quickly. The GitHub growth, from 9,000 to 182,000 stars in 60 days, is extreme by infrastructure standards, and for buyers it means the product is easier to inspect than a black-box managed service.

• It is broader than just code execution now. Desktop sandboxes, persistence, templates, and MCP support mean E2B is turning into a fuller agent runtime. That makes it more useful for teams who start with “run this Python” and later need longer-lived, tool-using workflows.

Weaknesses:

• Session limits are real. Hobby caps continuous runtime at 1 hour, and Pro at 24 hours. If you need always-on workers or jobs that run for days, E2B starts to feel like the wrong abstraction, and Northflank or a more traditional infrastructure setup may fit better.

• No GPU support. For most agent products this is fine because the model runs elsewhere, but it rules out some ML-heavy workflows. If your sandbox needs to train models or do accelerated local inference, E2B is not the tool we would point you to.

• Costs can become less intuitive at scale. The headline plans are simple, free or $150/month, but actual usage is compute-based and charged per second. That is fair, but teams with bursty or high-concurrency workloads need to model spend carefully rather than assume the subscription is the main cost.

• Enterprise deployment is not especially self-serve. E2B does support private cloud and enterprise setups, but you are talking to sales. If your team wants maximum control from day one and already has infrastructure staff, Northflank or self-managed approaches may feel more natural.

• Some competitors are better for persistent dev environments. Daytona, for example, is more opinionated around long-lived workspaces. If your agent behaves less like a code interpreter and more like a developer that needs a standing environment, E2B’s sandbox model may feel more transient than ideal.

Pricing

• Hobby: Free, includes $100 in compute credits
• Pro: $150/month
• Ultimate / Enterprise: Custom pricing

The free Hobby tier is one of E2B’s strongest adoption drivers. You get $100 in credits without a credit card, up to 20 concurrent sandboxes, creation at 1 sandbox per second, and up to 1 hour continuous runtime. For developers building a first agent or testing a code interpreter, that is enough room to learn the product properly before spending anything.

Pro starts at $150/month and raises the default concurrency to 100, with add-ons available up to 1,100 concurrent sandboxes. It also extends sandbox runtime to 24 hours. The important detail is that this is not all-you-can-eat pricing. E2B also charges for compute usage per second, based on CPU and RAM. We found example pricing around $0.000028 per second for a 2-core sandbox, plus roughly $0.0000045 per GB per second for memory.

In practice, that means small interactive apps can stay inexpensive for a long time, especially if sandboxes are short-lived. But if you are running many persistent sessions, costs will come from usage more than the plan fee. Compared with alternatives, E2B is easy to start with, but not something you should budget from the top-line subscription alone.

Alternatives

Daytona Daytona is one of the closest conceptual alternatives, but the philosophy is different. It leans more toward persistent workspaces and container-based environments, which can feel friendlier for long-running agent development. Teams may choose Daytona if they care more about stateful developer environments than the strongest possible isolation boundary. Teams choose E2B when security around untrusted code is the first question.

Vercel Sandbox Vercel Sandbox also uses Firecracker-style isolation and will appeal to teams already deep in the Vercel ecosystem. The tradeoff is scope. Vercel’s offering is more tied to Vercel’s platform and has shorter session limits, 45 minutes on Hobby and 5 hours on paid plans, based on the research we found. E2B looks better when you want longer sessions, broader agent-specific tooling, and less ecosystem lock-in.

Northflank Northflank is the choice for teams that want more direct infrastructure control, including self-hosted patterns and multiple isolation technologies. It is less of a plug-in agent runtime and more of a platform you shape yourself. That can be a better fit for enterprises with strong platform teams, but it also means more operational work than using E2B’s managed service.

Modal Modal is excellent for serverless compute and Python-heavy workloads, but it is not centered on AI-generated code execution in the same way. If you already know your workload and just need scalable compute, Modal may be simpler. If your main problem is “my model writes code and I need to run it safely,” E2B is more directly built for that job.

Microsandbox and similar sandbox APIs Smaller sandbox providers can be attractive on price or simplicity, especially for narrow use cases. The difference is usually maturity and ecosystem depth. E2B has stronger traction, an open-source core, enterprise references, desktop environments, and a clearer story around agent infrastructure as a category.

FAQ

What does E2B actually do?

It gives AI agents isolated cloud computers where they can safely run code, use files, access the internet, and perform tasks without touching your main app servers.

Who is E2B for?

Mostly developers and teams building AI agents, code interpreters, data analysis apps, and computer-use systems. It is also relevant for CTOs who need a secure execution layer before shipping agent features.

How do I get started?

Sign up for an account, get an API key, install the Python or JavaScript SDK, and create a sandbox from code. The free tier is enough for most first experiments.

How long does it take to set up?

For a basic sandbox, minutes. For a production setup with templates, persistence, and your own agent tooling, expect anywhere from a few hours to a few days.

Is E2B open source?

The core platform is open source. Most teams still use the managed cloud because running this kind of infrastructure yourself is a separate engineering project.

How fast are sandboxes to start?

E2B says sandboxes start in under 200 milliseconds. That is one reason it works well in interactive chat and agent flows.

Does E2B work with OpenAI only?

No. It is model-agnostic and can be used with OpenAI, Anthropic, Mistral, Groq, Hugging Face, Ollama, and others.

Can E2B run languages other than Python?

Yes. It supports Python, JavaScript/TypeScript, R, Java, and Bash out of the box, and templates can be used to add more runtimes.

Can I keep state between runs?

Yes. E2B supports persistent sessions and snapshots, so files, processes, and memory state can carry over instead of resetting every time.

Does E2B support desktop or browser automation?

Yes. Its desktop sandboxes provide a full Ubuntu environment with screenshot and input controls, which is useful for computer-use agents.

What are the main limitations?

The biggest ones are session limits, no GPU support, and usage-based pricing that needs attention at scale. It is strong for agent execution, less so for long-running always-on compute.

Is E2B good for enterprise use?

It appears to be. E2B says 88% of Fortune 100 companies have signed up or trialed it, and named users include Perplexity, Groq, Hugging Face, Vercel, LMArena, and Commure.