E2B
E2B runs isolated Linux sandboxes so AI agents can execute code, process data, and use tools safely.
E2B remains the execution layer we'd reach for first when an agent has to run untrusted code without a human babysitting containers. The $100 one-time Hobby credit makes evaluation genuinely free, and Pro at $150/mo with 24-hour sessions covers most production agent workloads before usage. Per-second metering is fair but variable — model your runtime before committing, and note Enterprise starts at $3,000/mo.
Verified 18h ago · liveness 80/100 · cite: rightaichoice.com/tools/e2b
- Developers building AI agents that must execute untrusted or model-generated code in isolation
- Teams running CI/CD or AI code review with one clean VM per GitHub Actions job
- Data-analysis copilots needing code interpretation plus persistent data volumes
- Enterprises that require sandboxed agent compute inside their own cloud boundary via BYOC or E2B Embed
- Teams wanting a no-code, fully managed analytics product rather than a compute primitive
- Buyers who need flat, predictable monthly pricing instead of per-second metered usage
- Projects without engineers available to wire an SDK into an existing application
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Skip E2B if you need flat, predictable monthly pricing rather than metered per-second sandbox billing, or if your workload is long-running persistent servers instead of ephemeral isolated sandboxes.
Usage is metered per second on top of any plan — vCPUs run $0.000014/s to $0.000112/s and memory $0.0000045/s to $0.0000360/s, so a large fleet running all month can dwarf the $150/mo Pro base.
E2B's pricing ladder starts free: Hobby costs $0/mo with a one-time $100 usage credit and no credit card, which puts it in reach of hobbyists and pre-seed teams that competitors like a managed cloud VM or a per-seat agent platform would price out immediately. Pro at $150/mo plus usage fits funded teams running production agent workloads, and Enterprise at a $3,000/mo minimum sits alongside other BYOC infrastructure contracts. Against a raw cloud VM, E2B costs more per compute-second but removes
In short
E2B — E2B runs isolated Linux sandboxes so AI agents can execute code, process data, and use tools safely. Best for Developers building AI agents that must execute untrusted or model-generated code in isolation, Teams running CI/CD or AI code review with one clean VM per GitHub Actions job, Data-analysis copilots needing code interpretation plus persistent data volumes. Free to start; paid plans from $150/mo.
What's new in E2B
Checked 7 days agoAcross the latest 5 updates: 1 launch, 2 changelog entries, 1 community discussion and 1 news mention.
E2B Embed packages runtime and dashboard for self-hosted sandboxes
E2B Embed packages the runtime and dashboard for one machine, letting you ship it in your product and run sandboxes inside your customer's environment.
Public project IDs, single Console for all regions, Embed OpenTelemetry export
Projects now expose prj_ IDs and workspaces wrk_ IDs; a single Console covers US, EU and APAC; Embed self-hosted can export telemetry via OpenTelemetry.
V2 sandbox create and connect endpoints plus CLI sandbox fork
SDK Sandbox.create and Sandbox.connect now call v2 endpoints and stop presetting omitted options; new CLI command forks sandboxes.
Guide: Build an Agent Workbench on OpenAI's Agents API
Guide builds an agent workbench on the OpenAI Agents API beta with E2B sandboxes, using application-managed lifecycle, one sandbox per chat, pause and fork.
Using E2B as the execution layer for Devin Outposts
Devin Outposts runs sessions inside customer infrastructure, with each session on an E2B microVM so execution stays within the cloud boundary.
What people actually say about E2B — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
45 mentions across 2 sources (Hacker News, Lemmy) · researched Jul 3, 2026.
Average across the 2 sources that answered — each source counts once, not each post.
- +Quick sandbox spin-up in seconds via SDKs or CLI.
- +Strong isolation with full Linux VMs per sandbox.
- +Open-source, no lock-in, can self-host on any cloud.
- +Rich features: snapshots, persistence, custom templates, Git integration.
- +Integrates smoothly with popular AI agent frameworks.
- −Cost can add up for high-volume or long-running sandboxes.
- −Sandbox resource limits may constrain heavy agent tasks.
- −Limited community presence outside Hacker News.
- −Pricing structure less transparent than some competitors.
- −Self-hosting requires additional setup and maintenance effort.
- • Overages for extra sandbox usage beyond plan limits.
- • Self-hosting incurs cloud infrastructure costs.
Viability Score
How well maintained and how widely used is E2B? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Isolated Linux sandboxes on the JavaScript/TypeScript and Python SDKs
- Per-second billing for vCPU, RAM, and storage
- Templates define base image, packages, and startup commands
- Pause and resume sandboxes with filesystem and memory preserved
- Paused sandboxes persist indefinitely until killed
- Snapshots, filesystem-only snapshots, and in-place forking
- CLI sandbox fork command for branching a running sandbox
- Interactive terminal with SSH access
- Public URLs for exposing sandbox services
- Data volumes for persistent storage across sandboxes
- Network controls: internet access, proxy tunneling, restricted access, SOCKS5 egress
- Network transform rules supporting leading wildcard domains
- V2 sandbox create and connect endpoints in SDK 2.51.0
- E2B Embed: self-hosted runtime and dashboard on Docker Compose, Terraform, or Kubernetes
- OpenTelemetry telemetry export, including for Embed
About E2B
E2B is sandbox infrastructure for AI agent code execution. Call Sandbox.create() from the JavaScript/TypeScript or Python SDK, get a fast Linux VM on demand, run commands in it, pause it, resume it, or fork it. Billing runs per second of a running sandbox, so an idle sandbox isn't an expensive one. The two building blocks are Sandbox, the VM itself, and Template, which defines the base image, packages, and startup commands the sandbox boots with. Pausing preserves filesystem and memory, and paused sandboxes stay around indefinitely until you kill them. On top of that sit interactive terminals with SSH, public URLs for exposing services, data volumes, filesystem operations, snapshots, in-place forking, and network controls that cover internet access, proxy tunneling, restricted access, and SOCKS5 egress through your own proxy. E2B Embed (September 2026) packages the runtime and dashboard so you can ship sandboxes inside your own or your customer's environment via Docker Compose, Terraform, or Kubernetes, with OpenTelemetry export for self-hosted telemetry. Since the September 21, 2026 changelog, SDK Sandbox.create and Sandbox.connect call v2 endpoints, and the CLI gained sandbox fork. The docs double as machine input: a public MCP server at docs.e2b.dev/mcp, a full page index at llms.txt, and Markdown source on every page. Console workspaces (wrk_ IDs) and project IDs (prj_ IDs) arrived September 28, 2026, with one Console covering US, EU, and APAC. Adoption isn't a claim here — the pricing page shows 1B+ sandboxes started, and case studies cover Perplexity, Manus, and Groq, plus Devin Outposts running each session on an E2B microVM inside the customer's cloud boundary. Pricing is freemium: a Free Hobby tier with a one-time $100 usage credit and no credit card, Pro at $150/mo, custom Enterprise with a $3,000/mo minimum. This is a compute primitive, not a finished analytics product — if you want a managed data-analysis UI, look elsewhere; if you need programmable
Behind the Verdict
Where E2B wins is the boring part everyone underestimates: isolation that doesn't require you to become a container platform team. We'd pick it when an agent needs to run model-written code, untrusted user code, or a full Linux desktop, and when per-second billing matches your actual usage pattern — bursty agent runs, not a permanently lit server. It's a strong fit for CI and code-review agents too. The GitHub Actions pattern is documented: one clean sandbox per job, run tests, tear down. If you're building an agent workbench on the OpenAI Agents API, the documented pattern of one sandbox per chat with application-managed lifecycle, pause, and fork is exactly what you want and exactly what's hard to build yourself. Where it bites is predictability. A $150/mo Pro base doesn't tell you your bill. Add concurrency — Pro+ is $500/mo for 600 concurrent, Pro++ $1,000/mo for 1,100 — and per-second compute on top, and a busy month can surprise finance. Run the workload estimator before you promise anyone a number. Pass on E2B if you need a no-code analytics product, if nobody on the team wants to wire an SDK into an application, or if your workload is a long-running persistent server rather than ephemeral sandboxes. It's infrastructure. It expects you to write code. The closest alternative depends on what you actually want. If you're already deep in a cloud provider's ecosystem and want one bill, their sandbox offerings may be simpler to procure. If you want a finished code-interpreter product, E2B is the wrong shape entirely. Where E2B differentiates is breadth — Template, snapshots, fork, volumes, network controls, Embed for BYOC, and a docs MCP server — plus integrations that span Claude Code, OpenAI Agents SDK, LangGraph, Google ADK, Mastra, and CrewAI. One practical
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Real-world workflow fit
Concrete scenarios for the personas E2B actually fits — and what changes day-one when you adopt it.
You install the Python or JavaScript SDK, set E2B_API_KEY, call Sandbox.create(), and run agent-generated Python through sandbox.commands.run(). You build a Template with the packages your agent needs so every sandbox boots ready.
Outcome: The agent executes untrusted code inside an isolated Linux microVM instead of your host, and per-second billing means you only pay while the sandbox is actually running.
You add E2B to a GitHub Actions workflow, spin up a clean sandbox per job, run tests and validation inside it, and tear it down. Paused sandboxes keep filesystem and memory state if a job needs to resume.
Outcome: Every CI job gets a pristine VM, so test pollution between runs disappears, and the workflow never touches your runners' host environment.
You use E2B Desktop sandboxes so the agent can see and control a virtual Linux desktop, expose services through public URLs, and route outbound traffic through SOCKS5 or a proxy with wildcard network transform rules.
Outcome: The agent drives a real desktop environment without giving it access to any host machine, and you can restrict or tunnel its network egress.
Use Cases
- Run untrusted code from AI agents in isolated sandboxes to prevent host system breaches.
- Automate CI/CD testing and AI code reviews by spinning up sandboxes in GitHub Actions workflows.
- Enable AI models to interpret and execute Python/JavaScript code to analyze data.
- Build computer-use agents that control virtual Linux desktops for browser automation or desktop tasks.
- Create cloud browsers or proxy tunneling services within secure sandboxes for web scraping or testing.
- Deploy temporary coding environments for educational platforms or hackathons that auto-destroy after use.
- Run multi-agent workflows in a secure execution layer, as done by Effective AI in insurance.
- Use as the execution layer for Devin Outposts to run Devin's agentic workflows inside your own cloud boundary.
Limitations
- Usage is billed per second beyond the base subscription, so extensive or long-running sandboxes add cost on top of the $150/month Pro base.
- Hobby sessions are capped at 1 hour and 20 concurrent sandboxes; Pro raises these to 24 hours and 100 concurrent, with larger concurrency requiring paid add-ons (Pro+ 600 concurrent for +$500/mo, Pro++ 1,100 concurrent for +$1,000/mo), and Enterprise requires a $3,000/mo minimum.
- Custom CPU/RAM configurations are Pro-tier and above.
- A control-plane failure on January 13, 2026 blocked new sandbox creation until the postmortem fixes shipped — self-hosting via E2B Embed is the mitigation.
- Volumes and Secrets remain in private beta.
- The SDK 2.46.0 release deprecated the git module in favor of commands.run, and CLI 2.18.0 introduced a breaking change where template commands take the template as an argument instead of reading e2b.toml.
as of 2026-09-26
Verification history
We have re-verified E2B 10 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 10 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published E2B tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Hobby
$0/mo + Usage Costs
Ideal for
Solo developer or pre-seed team prototyping an agent that needs code execution, using the one-time $100 credit to test without a card.
What this tier adds
Free entry point: $0/mo plus usage, 1-hour max sessions, 20 concurrent sandboxes, default CPU/RAM, and 10 GiB free storage.
Pro
$150/mo + Usage Costs
Ideal for
Funded team running E2B in production for an agent product, where 24-hour sessions and custom CPU/RAM matter.
What this tier adds
Adds $150/mo base over Hobby: 24-hour sessions, 100 concurrent sandboxes expandable to 1,100 via add-ons, custom CPU/RAM, and 20 GiB free storage.
Pro+ concurrency addon
+$500/mo
Pro++ concurrency addon
+$1,000/mo
Enterprise
Custom + Usage Costs
Ideal for
Large org needing committed usage, BYOC deployment, or tens of thousands of concurrent sandboxes inside its own cloud boundary.
What this tier adds
Custom pricing over Pro with a $3,000/mo minimum: multi-day and persistent sessions, custom sandbox sizes, BYOC, and custom billing.
Where the pricing makes sense
The company stage and team size where E2B's pricing actually pencils out — and where peers do it cheaper.
E2B's pricing ladder starts free: Hobby costs $0/mo with a one-time $100 usage credit and no credit card, which puts it in reach of hobbyists and pre-seed teams that competitors like a managed cloud VM or a per-seat agent platform would price out immediately. Pro at $150/mo plus usage fits funded teams running production agent workloads, and Enterprise at a $3,000/mo minimum sits alongside other BYOC infrastructure contracts. Against a raw cloud VM, E2B costs more per compute-second but removes
Setup time & first value
How long it actually takes to get something useful out of E2B — broken out by persona, not the marketing-page minute.
For a developer already holding an API key: under 10 minutes to a running sandbox, since Sandbox.create() plus sandbox.commands.run() is the whole quickstart. For a production agent with a custom environment, budget a few hours to build and publish a Template with your base image, packages, and startup commands. Self-hosted E2B Embed via Docker Compose, Terraform, or Kubernetes is a day-scale
Switching to or from E2B
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From a self-managed Docker or Kubernetes container runtime: replace your container lifecycle code with Sandbox.create() and pause/resume, keeping isolation guarantees you previously hand-rolled.
- →From raw cloud VMs (EC2/GCE): move ephemeral agent workloads to E2B sandboxes and keep only long-running services on the VM.
- →From AWS Lambda or other serverless functions: use E2B when the function needs a real filesystem, a shell, and packages a static runtime can't carry.
- →From a competing code-interpreter API: port your execution calls to the SDK's commands.run and filesystem methods, then move base images into Templates.
- →From GitHub Actions runners: swap job steps to spin up E2B sandboxes inside the workflow for per-job isolation.
- ↗To a self-hosted container platform: if you want full control of the host, deploy your own KVM or container stack and rebuild the pause, fork, and snapshot primitives.
- ↗To a fully managed analytics product: if you want a finished no-code tool rather than a compute primitive, move to an end-user analytics platform.
- ↗To a flat-fee cloud VM: if your workload is long-running and predictable, a reserved VM may cost less than metered per-second sandbox billing.
- ↗To a different agent execution API: port commands.run and filesystem calls to the new SDK, and recreate your Templates as that platform's image definitions.
Integrations
Resources & Guides
- Documentatione2b.dev
Docs · E2B
Full product docs from e2b.dev
- Quickstarte2b.dev
Quickstart · E2B
Get up and running fast from e2b.dev
- Documentatione2b.dev
Sdk Reference · E2B
Full product docs from e2b.dev
- Documentatione2b.dev
Api Reference · E2B
Full product docs from e2b.dev
- Documentatione2b.dev
Changelog · E2B
Full product docs from e2b.dev
- Documentatione2b.dev
Faq · E2B
Full product docs from e2b.dev
- API Referencee2b.dev
Cli · E2B
Methods, params, types from e2b.dev
- Documentatione2b.dev
Cookbook · E2B
Full product docs from e2b.dev
- Resourcedocs.e2b.dev
Mcp · E2B
Helpful link from docs.e2b.dev
- Resourcedocs.e2b.dev
Llms · E2B
Helpful link from docs.e2b.dev
Tutorials & Learning
YouTube returned 6 videos for “E2B”, and we withheld 6: 6 could not be judged, because “E2B” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about E2B.
Official links
Tools that pair well with E2B
Common stack mates teams adopt alongside E2B, with the specific reason each pairing earns its keep.
Daytona
Run AI-generated code in isolated sandboxes that start in under 90ms, billed per second with $200 in free compute.
Fly.io
Hardware-isolated Linux VMs called Sprites that checkpoint automatically for AI agents, billed per second of active use.
Rover
Rover runs multiple AI coding agents in parallel, each in its own isolated local sandbox.
Featured Head-to-Head Comparisons
E2b vs Spider Cloud
Choose E2B if your AI agent needs to safely execute code in isolated sandboxes with fine-grained control — ideal for coding agents, CI/CD, and interactive terminals. Choose Spider Cloud if your priority is fast, affordable web scraping and structured data extraction for RAG or LLM training, with recent Browser AI commands making it easier to interact with pages. Both are open-source, freemium, and integrate with LangChain.
E2b vs Temporal Ai
Both tools are freemium but serve different needs: Temporal ensures durable, fault-tolerant orchestration for long-running AI agents and microservices; E2B provides secure, ephemeral sandboxes for code execution within AI workflows. Choose Temporal if you need guaranteed completion and state persistence across failures; choose E2B if you need isolated, temporary environments for AI agents to run code safely. They can complement each other in a stack.
E2b vs Presto Voice
Choose Presto Voice if you run a QSR chain seeking drive-thru automation with proven revenue uplift (up to 6% monthly). Choose E2B if you're a developer needing secure, ephemeral sandboxes for AI agent code execution—E2B offers flexible, per-second pricing and deep integration with agent frameworks, while Presto is a turnkey enterprise solution for drive-thru.
Alternatives to E2B
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