# How Genspark uses E2B

**$250M ARR**: “from zero to $250M ARR in 12 months”

As published on [e2b.dev](https://e2b.dev/customers/genspark), May 2026. Captured by usedby on 2026-10-06.

- Company: [Genspark](https://www.usedby.ai/companies/genspark.md)
- Tool: [E2B](https://www.usedby.ai/tools/e2b.md)
- Teams: Engineering

## What the story says

Genspark runs its general-purpose Super Agent on E2B's Firecracker micro VMs, giving each agent session an isolated virtual machine to execute code, install packages, browse the web and keep state across long multi-step tasks. This lets its engineers focus on the agent rather than building their own sandbox infrastructure.

Summary written by usedby from the source page, in English. The figures are those of E2B and Genspark, not ours.

> E2B lets Genspark’s engineering team focus entirely on making the agent smarter and more capable, while E2B handles the execution layer: spinning up isolated virtual machines, managing resources, ensuring security, and scaling to millions of sessions.

> A 30-step agent run can't start with a 60-second cold start. It has to feel instant. E2B lets us scale to thousands of concurrent sessions, and we couldn't have hit $250M ARR if five of our engineers were building a sandbox platform instead of the agent.
>
> Kay Zhu, Co-founder & CTO

## What usedby checked

We compared the story with its live page on 2026-10-06.

- Checked: the figure $250M ARR is printed word for word on the page, near the name of Genspark.
- Checked: the passage quoted above is copied word for word from the page, near the name of Genspark.
- Checked: each number in our summary is printed on the page.
- Checked: the publication date is read from the page’s own metadata, never guessed.
- Checked: Genspark uses E2B. Confirmed line. Latest check across sources: 2026-10-06.
- Not checked: the result itself. We quote it; we did not measure it.

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Source: https://www.usedby.ai/case-studies/genspark-e2b · How we check: https://www.usedby.ai/methodology