The disposable computer for AI. E2B hands every agent a secure Linux sandbox that boots in under 200 milliseconds - then throws it away.
Here is the awkward secret behind every viral AI demo. An agent writes some Python. The Python does something clever - scrapes a page, crunches a spreadsheet, builds a chart. And then, somewhere, on some machine, that freshly-generated code has to actually run. Which is a problem, because code an AI wrote five seconds ago is, from a security standpoint, code written by a stranger. You would not run a stranger's script on your production server. Neither would JPMorgan.
E2B is the company that decided this boring, unglamorous problem was worth building a business around. Its product is a sandbox: a small, isolated Linux computer that spins up on demand, lets an AI agent do whatever it needs to do, and then evaporates. Nothing the agent touches can reach anything it shouldn't. The blast radius is exactly one throwaway virtual machine.
The clever part is speed. E2B builds on Firecracker, the same microVM technology Amazon wrote to power AWS Lambda. That lets an E2B sandbox boot in under 200 milliseconds - faster than you can blink, fast enough that an agent doesn't feel like it's waiting for a computer to turn on. When you're orchestrating thousands of agent steps, those milliseconds compound into the difference between usable and useless.
None of this is the part of AI that gets magazine covers. There's no chatbot personality, no model with a clever name. E2B sells the plumbing. But the plumbing turns out to be where a lot of the money quietly sits: when the models are commodities and everyone has an agent, the durable question becomes where does it all run - and the answer, for a striking share of large companies, is E2B.
The traction reads like an infrastructure land-grab. E2B says roughly 88% of the Fortune 100 have signed up, usage spans more than half the Fortune 500, and the company has run hundreds of millions of sandboxes since late 2024. The SDKs are downloaded over two million times a month. When one investor called E2B the "iOS for AI agents," the comparison was less about the phone and more about the layer nobody sees but everything depends on.
"In 10 years, we see AI agents becoming as commonplace as apps on your iPhone." - E2B, on its Series A
E2B's founders, Vasek Mlejnsky and Tomas Valenta, have been coding together since they were about twelve. They trained as researchers in the Czech Republic, worked on computer vision, and eventually moved to San Francisco. The company that stuck wasn't the flashy AI idea - it was the infrastructure underneath one.
The public voice of E2B, making the case that AI agents will need dedicated runtimes the way apps needed the App Store. Leads product and go-to-market from San Francisco.
The engineering half of the partnership, responsible for the sandbox infrastructure - the microVM orchestration that has to be fast, isolated, and boringly reliable at massive scale.
An intelligent model with nowhere to act is just conversation. E2B gives an agent somewhere to act - a real computer it can use and then discard. Here is what developers reach for it to do.
On-demand, microVM-isolated Linux boxes that boot in under 200ms. The safe place your agent's code actually runs.
SDKs that let an AI app execute generated code, install packages, and return results - inside isolation, one call away.
A full virtual desktop an LLM can drive - clicking, browsing Chrome, using apps - without ever escaping the sandbox.
Secrets management, sandbox observability, shared context, and BYOC / on-prem deployment for regulated teams.
| Round | Amount | Date | Lead / Notable |
|---|---|---|---|
| Seed | ~$11.5M | 2024 | Decibel, Sunflower Capital |
| Series A | $21M | Jul 2025 | Insight Partners |
The July 2025 Series A was led by Insight Partners, with Decibel, Sunflower Capital and Kaya returning, plus angels including former Docker CEO Scott Johnston. The stated plan: expand engineering, product and go-to-market in San Francisco, and harden the platform for enterprise.
E2B names AI-native companies and hyper-growth startups among its users - the kind of names that show up in the agent conversation constantly.
One reported enterprise result: document-processing agents saving on the order of 360,000 hours of manual work a year - all executing in code that runs, and then disappears.
Mlejnsky and Valenta start E2B in San Francisco, aiming at secure runtimes for AI agents.
Seed round closes; Python and TypeScript code-interpreter SDKs ship; sandbox usage begins scaling into the hundreds of millions.
Insight Partners leads a $21M round, lifting total funding to ~$32M as Fortune 100 adoption reaches ~88%.
E2B rolls out Secrets Vault, Sandbox Observability, Shared Context and BYOC / on-prem deployment for regulated customers.