E2B is the open-source cloud that gives AI agents secure, real computers — Firecracker microVM sandboxes that boot in ~150 ms, run for up to 24 hours, and let an LLM execute code, browse the web, or click through a desktop without you ever trusting it on your own machine. We rate it 88/100 — the clear category leader for agentic code execution today, with the only meaningful caveats being that it is still a young company and the runtime cost adds up at scale.

What is E2B?

E2B (which stands for "Environments to Build" agents) was founded in March 2023 by Czech founders Vasek Mlejnsky (CEO) and Tomas Valenta (CTO). The company has raised $32.5M to date — an $11.5M seed in September 2023 led by Decibel Partners and a $21M Series A in July 2025 led by Insight Partners with participation from Decibel, Sunflower Capital, Kaya, and angels including former Docker CEO Scott Johnston. The open-source e2b-dev/E2B repository on GitHub has crossed 12,000 stars and 880+ forks, and the company's homepage claims usage by 88% of Fortune 100 companies, with public case studies from Perplexity (advanced data analysis shipped in one week), Manus (virtual computers for autonomous agents), Hugging Face, Notion, and Y Combinator portfolio companies.

The problem E2B solves is simple to state and brutal to solve: when an LLM writes code, you do not want to exec() it on your laptop or in your production VPC. You want a disposable, fully isolated machine that boots fast enough to feel synchronous, exposes a real filesystem, network, and shell, and tears down cleanly when the agent is done. E2B is the platform that turns that requirement into a one-line SDK call: Sandbox.create() in Python or TypeScript and you have a Linux box your agent can do anything in.

E2B — The Enterprise AI Agent Cloud, share image showing Firecracker microVM sandbox for AI agents — E2B markets itself as "The Enterprise AI Agent Cloud" — Firecracker-based sandboxes that 88% of Fortune 100 companies now use for agentic workflows.

Key Features of E2B

~150 ms cold starts: Each sandbox is a Firecracker microVM, the same hypervisor AWS Lambda is built on. A fresh Linux box with internet, filesystem, and a shell is ready before a typical LLM has finished its first token, which is why E2B feels synchronous from inside an agent loop.
Up to 24-hour session length: Hobby tier sessions can run for up to 1 hour; Pro tier extends that to 24 hours, which covers virtually every long-running data-analysis or browser-automation workflow without requiring custom orchestration on your side.
Polished Python and TypeScript SDKs: The Code Interpreter SDK is the spiritual descendant of ChatGPT's data analysis tool — feed an agent's code in, get stdout, file artifacts, and rendered Matplotlib/Plotly charts back. Both SDKs are MIT-licensed.
E2B Desktop: A separate sandbox flavor with a full graphical Linux desktop (Xfce + noVNC), built specifically for "computer use" agents like Anthropic's Computer Use API and OpenAI's Operator. The agent gets a screen it can see, a mouse it can move, and a keyboard it can type on.
Custom templates: Define a sandbox template with a Dockerfile and any system packages, Python wheels, or npm dependencies you need pre-installed. New sandboxes spawned from that template still cold-start in milliseconds.
20 concurrent sandboxes on the free Hobby tier: Plenty for development, demos, and side projects. Pro lifts the concurrency cap and adds customizable CPU and RAM per sandbox.
Open source, self-hostable enterprise tier: The entire infra layer is Apache-2.0 on GitHub. The Enterprise plan offers BYOC (bring-your-own-cloud), on-prem, or fully self-hosted deployments — important for regulated industries that cannot send code off-site.
Works with any LLM: Native examples and integrations for OpenAI, Anthropic, Llama, Mistral, LangChain, LlamaIndex, Vercel AI SDK, and CrewAI. E2B is deliberately model-agnostic — it sits below your model layer.

E2B GitHub repository — open-source secure environment for enterprise-grade AI agents — The open-source e2b-dev/E2B repo on GitHub: 12K+ stars, Apache-2.0, and the foundation under E2B's hosted cloud.

What Users Say About E2B

Sentiment across Hacker News, r/LocalLLaMA, and developer Twitter is unusually positive for an early infrastructure company. The most consistent praise: the SDKs "actually feel good to use," documentation is dense and accurate, and support resolves issues "in minutes" — a recurring quote from enterprise testimonials. On the Insight Partners and VentureBeat coverage of the Series A, developers from Perplexity and Manus publicly credited E2B for shipping features in days that would have taken months on a custom orchestration layer.

The honest complaints are also consistent. Reddit threads in r/LangChain and r/AI_Agents flag two real concerns: per-second runtime billing (~$0.000168/sec per sandbox) gets expensive for high-volume workloads — at 100K runs/month at 30 seconds each, you are paying around $504/month in sandbox time before LLM costs — and the dashboard offers more configuration than a solo developer needs for a quick Python script. Some commenters on the open-source repo have asked for longer free-tier sessions and a clearer migration path to self-hosting; both are roadmap items the company has acknowledged.

E2B Pricing

E2B's pricing has three tiers, all on top of per-second usage costs. The Hobby tier is genuinely usable thanks to the $100 of free credits, and the Pro tier is priced for teams already shipping agents in production.

Plan	Price	Key Limits
Hobby	Free + $100 one-time credit	1-hour max sessions, 20 concurrent sandboxes, community support, no credit card
Pro	$150/month + usage	24-hour sessions, customizable Sandbox CPU & RAM, higher concurrency, priority support
Enterprise	Custom	BYOC, on-prem, or self-hosted; SOC 2; dedicated support; volume discounts on runtime

Sandbox runtime is billed at roughly $0.000168 per CPU-second. For mental math: a sandbox running for one minute costs ~$0.01. The free $100 credit translates to about 165 hours of single-CPU sandbox time, which is more than enough to ship a real product to MVP.

Who Should Use E2B?

Best for: Teams shipping AI agents that need to execute code, run browser automation, or perform data analysis on user-supplied inputs. SaaS products adding a "ChatGPT-style code interpreter" feature. Anyone building a coding agent, a data analyst agent, or a "computer use" agent on top of Claude, GPT, or Gemini. Open-source projects that want a turnkey sandbox layer without writing their own Firecracker orchestrator.

Not ideal for: Hobbyists running one-off Python scripts where a local Jupyter notebook is fine. Workloads that need persistent state across long-running sessions — sandboxes are meant to be ephemeral. Cost-sensitive use cases that run code for hours at extreme concurrency without negotiating an Enterprise rate.

Pros and Cons

Pros:

Best-in-class cold start (~150 ms) — the main thing that distinguishes E2B from running your own container farm
Open source under Apache-2.0 with a credible self-hostable path for regulated buyers
SDKs and docs are widely praised; developers report "one-hour" integration times
Enterprise traction is real — Perplexity, Manus, and other named customers are on the homepage with case studies, not stock photos
$100 free credit is enough to ship a side project end-to-end without paying anything

Cons:

Per-second billing scales linearly — long-running sandboxes at high concurrency get expensive
Sessions on Hobby tier cap at 1 hour, which can bite you if you forget
Still a young company (founded 2023) — uptime and feature velocity have been excellent so far, but there is less battle-testing than with a Lambda or Cloud Run
The dashboard surface area is heavier than a solo developer needs, particularly around custom templates and team management

Alternatives to E2B

Modal is the closest competitor — it is more general-purpose serverless compute, not agent-specific, but you can build a sandbox layer on top. Daytona ships Firecracker-based developer environments and recently added an AI sandbox SDK, so it is increasingly a head-to-head choice. Cloudflare's Workers Sandbox API is cheaper for very short-lived JS workloads but lacks E2B's full Linux environment. For self-hosted options, beam.cloud, Sprites.dev, and rolling your own Firecracker setup are all on the table — none have E2B's polish out of the box.

Verdict: Is E2B Worth It?

If you are building an AI agent in 2026 that needs to execute untrusted code or operate a real computer, E2B is the default answer and probably the right one. The 150 ms cold start, the SDK quality, and the production references from Perplexity and Manus all justify the price. Hobbyists with the $100 free credit get a free runway long enough to ship a working product. The case to look elsewhere only really applies if you are running massive concurrent workloads where the per-second cost compounds, or if you have a hard regulatory requirement that pushes you toward self-hosted from day one — and even then, E2B's Apache-2.0 license and Enterprise self-hosted plan keep them in the conversation. We rate it 88/100: very good, market-leading where it matters, with honest room to grow on long-running sessions and price for high-volume use.

Frequently Asked Questions

Is E2B free?: Yes. The Hobby tier is free and ships with a one-time $100 in usage credits, no credit card required. After the credit runs out you pay per-second runtime; paid Pro plans start at $150/month.
Is E2B open source?: Yes — the core sandbox infrastructure is Apache-2.0 licensed at github.com/e2b-dev/E2B, with 12,000+ stars. SDKs (Python and TypeScript) are MIT-licensed.
What is the difference between E2B Sandbox and E2B Desktop?: E2B Sandbox is a headless Linux microVM for code execution. E2B Desktop adds a graphical Xfce desktop with VNC access, designed specifically for "computer use" agents like Anthropic's Computer Use API or OpenAI's Operator that need to see and click a real screen.
How does E2B compare to Modal?: E2B is purpose-built for AI agents and ships with agent-specific SDKs (Code Interpreter, Desktop, browser tools). Modal is general-purpose serverless Python compute and is excellent for ML inference and batch jobs but requires more glue code to use as an agent sandbox. For pure agent workloads, E2B is faster to integrate.
What platforms does E2B run code on?: E2B sandboxes are Linux microVMs running on x86_64. You access them via the cloud from any platform — Python and TypeScript SDKs work on macOS, Windows, and Linux. The agent's code runs inside the Linux sandbox.
Can I self-host E2B?: Yes, on the Enterprise plan, which supports BYOC (bring-your-own-cloud), on-prem, and fully self-hosted deployments. The open-source repo also lets technically-inclined teams run their own infra.

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