AI visibility report
AI visibility report for E2B in AI Code Sandboxes & Agent Runtimes.
Outside the top three on 19 of the 25 prompts buyers actually ask.
Northflank is cited on 19 of those losses.
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Track E2B across these prompts daily.
Start free trialStill absent from 94.7% of tracked prompt responses
Top-3 citations across 150 prompt × platform pairs
Peer Ranking
Key Metrics
Platform Breakdown
ChatGPT
Perplexity
Gemini Search
Bing Copilot
Google AI Mode
GrokHow to read this. E2B appears in 5.3% of tracked prompt responses. Presence is absolute coverage; share of voice is relative citation share; sentiment measures tone only when the brand appears.
Where E2B is losing
Prompts where competitors are visible and E2B is not.
These prompt-level losses are the first prompts to track and repair.
Where E2B is winning2
What's the fastest sandbox runtime to spin up for an AI agent backend — which platforms let you get isolated code execution running in under 5 minutes?
Avg # 1.0 · 1 platform
Which microVM-based sandbox platforms have the smoothest onboarding for a solo developer shipping an AI coding assistant MVP?
Avg # 1.0 · 1 platform
Where E2B is losing5
Which code sandbox platforms are considered production-ready for enterprise AI applications where uptime and SLA guarantees actually matter?
Competitors on 5 platforms
Track this promptMy AI agent generates and executes code in a tight loop — which sandbox platforms sustain high-frequency execution without degrading over time?
Competitors on 5 platforms
Track this promptWhich code sandbox services have good observability built in so I can actually debug what my AI agent is running inside the environment?
Competitors on 5 platforms
Track this promptLooking for an ephemeral code execution environment I can provision per user session — which services have a simple SDK or API to get started quickly?
Competitors on 4 platforms
Track this promptI'm evaluating sandboxed agent runtimes for a small team building an AI data analyst tool — what should I look at to avoid the overhead of self-hosting?
Competitors on 4 platforms
Track this prompt
Track E2B daily before the next report refresh.
Track these gapsResearch dossierCapabilities, use cases, sources, reviews, pricing, and FAQ
Overview
E2B (FoundryLabs, Inc.) is an open-source cloud infrastructure platform providing secure, isolated sandbox environments purpose-built for AI agents and LLM-powered applications. Founded in 2023 by Vasek Mlejnsky and Tomas Valenta, E2B uses Firecracker microVMs to spin up fully isolated Linux environments in under 200 milliseconds. Its Python and JavaScript/TypeScript SDKs allow developers to integrate sandbox-based code execution into any LLM or AI framework with minimal setup. Core use cases include AI coding agents, deep research, data analysis and visualization, computer use, reinforcement learning, and vibe coding. Trusted by approximately 88% of Fortune 100 companies—including Perplexity, Hugging Face, Groq, and Manus—the platform has processed over 500 million sandboxes and sees 2 million+ monthly downloads. E2B raised a $21M Series A in July 2025 (total ~$32M), led by Insight Partners.
E2B is an open-source AI agent cloud platform that provides on-demand, Firecracker microVM-isolated sandbox environments for securely executing AI-generated code and running autonomous agent workflows. It offers Python and TypeScript SDKs, a code interpreter, a Desktop Sandbox for computer use, MCP gateway support, and enterprise BYOC/on-prem deployment—all accessible in a few lines of code and used in production by Fortune 100 enterprises.
Key Facts
- Founded
- 2023
- HQ
- San Francisco, CA / Czech Republic (dual presence)
- Founders
- Vasek Mlejnsky, Tomas Valenta
- Employees
- 28-30
- Funding
- ~$32M
- Customers
- 88% of Fortune 100 signed up; used acros
- Status
- Private
Target users
Key Capabilities10
- Firecracker microVM-based isolated sandbox environments with hardware-level isolation per session
- Sub-200ms sandbox startup with no cold starts
- Python and JavaScript/TypeScript SDKs for sandbox creation and management
- Code interpreter supporting Python, JavaScript, Ruby, C++, and any Linux-compatible language
- Desktop Sandbox for LLM-powered computer use agents (graphical Linux desktop)
- Custom sandbox templates with versioning, caching, and package pre-installation
- Sessions up to 24 hours; sandbox persistence, snapshots, and auto-resume
- Bring Your Own Cloud (BYOC) and self-hosted/on-prem deployment options
- MCP gateway for secure Model Context Protocol server hosting
- Concurrent sandbox scaling to hundreds of instances for reinforcement learning and batch workloads
Key Use Cases8
- AI coding agents that execute, debug, and iterate on code
- Deep research agents conducting multi-step data retrieval and analysis
- AI data analysis and chart/visualization generation
- Computer use agents controlling virtual Linux desktops
- Reinforcement learning with tens of thousands of concurrent sandboxes
- Vibe coding / AI-generated app runtimes (any language or framework)
- Workflow automation with embedded secure code execution (e.g., no-code SaaS platforms)
- Secure CI/CD pipelines powered by AI-driven testing and validation
E2B customer outcomes
Millions of E2B sandboxes run per month at 340M searches/month scale; feature shipped in 1 week
Perplexity integrated E2B to power secure code execution for advanced data analysis features for its Pro users. The integration went from first steps in July 2024 to production in August 2024, with E2B up and running within one week.
Integration completed in ~0.5 days; avoided 3–5 FTE engineers of infra build
Manus used E2B to provide its multi-agent AI system with full virtual computers, enabling 27 tools and complex autonomous workflows. Integration took half a day versus an estimated 3–5 FTE engineer-months to build in-house.
Shipped in 1 week with 1 engineer; estimated 'weeks and multiple people' avoided
Lindy integrated E2B to power a new Code Action feature enabling custom Python and JavaScript execution within no-code workflows. Implementation took one engineer working in spare cycles for one week, versus an estimated weeks of work for multiple engineers in-house.
Tens of thousands of concurrent sandbox machines with near-zero setup time
Hugging Face used E2B to securely scale AI research experiments, launching tens of thousands of concurrent machines with near-zero setup time to replicate DeepSeek-R1 training.
Recent Trend
How AI describes E2B3
...| gVisor isolation, GPU elastic scaling | ~1s | Very low (serverless, Python-first) | ML-heavy workloads needing GPUs | | E2B | Firecracker microVMs | ~80ms | Low (SDK-driven, session-based) | Developers embedding agent sandboxes in apps | | Clou...
What do platform engineers typically use to manage ephemeral execution environments for AI agents — and which options have the least operational burden?
Fastest options today: Cloudflare Workers, Blaxel, and E2B can spin up isolated runtimes in well under 5 minutes — often sub‑second — while heavier VM‑based sandboxes like Modal or Northflank take 2–3 seconds.
What's the fastest sandbox runtime to spin up for an AI agent backend — which platforms let you get isolated code execution running in under 5 minutes?
| | E2B | Firecracker microVM isolation, clean SDKs, filesystem + RAM snapshots.
What are the best isolated runtime options for AI agents that need persistent filesystem state across multiple execution steps in a single session?
Most cited sources7
28E2B | The Enterprise AI Agent Cloud
e2b.dev·Landing Page
- 13
E2B | The Enterprise AI Agent Cloud
e2b.dev·Landing Page
- 10
Code Interpreter Sandbox — E2B Blog
e2b.dev·Landing Page
7GitHub - e2b-dev/E2B: Open-source, secure environment with real-world tools for enterprise-grade agents. · GitHub
github.com·Product Page
- 3
Documentation - E2B
e2b.dev·Landing Page
- 3
Documentation - E2B
e2b.dev·Landing Page
Alternatives in AI Code Sandboxes & Agent Runtimes6
E2B positions itself as the open-source, enterprise-grade standard for AI agent sandboxes—purpose-built for agentic and LLM workflows using Firecracker microVM isolation.
- Unlike general cloud platforms or container-based runtimes, E2B targets AI-native use cases (coding agents, deep research, computer use, RL training) with sub-200ms sandbox startup, an LLM-agnostic SDK, and a path to BYOC/on-prem deployment.
- It differentiates from Modal (gVisor-based, broader AI infra platform), Daytona (Docker containers, workspace-first), and Northflank (longer-lived, more mature BYOC) by combining hardware-level microVM isolation with polished Python and TypeScript SDKs and a developer-first, open-source community strategy.
Reviews
Praised
- Best-in-class Python and TypeScript SDK quality
- Fast sandbox startup (~150ms, no cold starts)
- Strong Firecracker microVM security/isolation
- Easy integration (often described as a few hours to one week)
- Active open-source community and responsive engineering team
- LLM-agnostic and framework-flexible
- Extensive cookbook examples and documentation
Criticized
- Session duration limits (1h Hobby, 24h Pro) disruptive for long-running agents
- Strict concurrency caps require paid add-ons at scale
- BYOC immature; self-hosting requires managing E2B infra independently
- Limited regional/infrastructure flexibility on managed tier
- No native GPU support
- Pricing adds up at high sandbox volume
- Lack of built-in long-session orchestration compared to workspace-first platforms
No verifiable scores on major review platforms (G2, Gartner Peer Insights) were found as of this research. Developer sentiment gathered from third-party comparison articles and community discussion is broadly positive: E2B is consistently cited as the category pioneer with the best developer experience and SDK quality among AI sandbox runtimes. Common criticisms focus on session timeout constraints, concurrency limits at scale, and BYOC maturity relative to alternatives like Northflank.
Pricing
Free Hobby tier includes a one-time $100 usage credit, community support, sandbox sessions up to 1 hour, and up to 20 concurrent sandboxes (no credit card required). Pro plan is $150/month and adds 24-hour session lengths, up to 100 concurrent sandboxes (expandable to 1,100), and customizable CPU and RAM. Enterprise tier offers custom pricing with BYOC, on-prem, and self-hosted options. Usage is billed per second: 2 vCPU (default) costs $0.000028/s (~$0.10/hour); RAM is $0.0000045/GiB/s. Storage is free up to 10 GiB (Hobby) or 20 GiB (Pro).
Limitations
- Session duration capped at 1 hour (Hobby) and 24 hours (Pro), which can disrupt long-running reasoning tasks.
- Concurrency limits of 20 (Hobby) and 100 (Pro) sandboxes; higher limits require add-on purchase.
- BYOC is currently limited to AWS and enterprise-tier customers; self-hosting requires deploying and managing E2B infrastructure independently.
- No native GPU support (as of early 2026), limiting suitability for GPU-intensive AI workloads.
- Regional infrastructure flexibility is limited on the managed cloud tier.
- Some third-party evaluations note the platform lacks built-in orchestration for persistent multi-session agent workflows compared to workspace-focused alternatives.
Frequently asked questions
Topic coverageCoverage by buyer topic
Topic Coverage
Prompt-Level Results
| Prompt | ChatGPT | Bing Copilot | Perplexity | Google AI Mode | Gemini Search | Grok |
|---|---|---|---|---|---|---|
Capability1/5 cited (20%) | ||||||
Looking for a sandboxed code interpreter that can handle long-running jobs — 10 to 30 minutes — without hitting timeout limits. What are my options? | Your brand and a competitor were cited | A competitor was cited | Your brand and a competitor were cited | Neither your brand nor a competitor was cited | Your brand was cited | Neither your brand nor a competitor was cited |
Which sandboxed execution platforms let AI agents run arbitrary shell commands safely without kernel-level escape risks or shared-tenant interference? | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
Which agent runtime platforms support spawning concurrent sandbox instances so multiple AI agents can run code in parallel for a multi-agent workflow? | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
I need a code execution environment that supports GPU workloads for AI-generated training scripts — which sandboxed platforms handle that use case? | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
What are the best isolated runtime options for AI agents that need persistent filesystem state across multiple execution steps in a single session? | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
Developer Experience0/5 cited (0%) | ||||||
Which agent compute platforms have the most active developer communities and solid docs for teams just getting into agentic AI workflows? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
What do platform engineers typically use to manage ephemeral execution environments for AI agents — and which options have the least operational burden? | A competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
I want a sandboxed runtime where my team can define reusable execution templates — which platforms make that workflow easy without deep infra knowledge? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Which code sandbox services have good observability built in so I can actually debug what my AI agent is running inside the environment? | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
Which AI sandbox platforms offer the best developer experience for iterating on agent tools locally before deploying to production? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
Integrations & Ecosystem2/5 cited (40%) | ||||||
What are the best code execution sandbox options that support pre-installing custom dependencies from a private package registry before agent runs? | Your brand and a competitor were cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
What sandboxed execution environments have good support for streaming output back to the calling application in real time during an agent's code run? | Your brand and a competitor were cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Which agent compute platforms avoid heavy lock-in and work across major cloud providers so I can keep data residency in my existing infrastructure? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Which sandboxed agent runtimes integrate well with popular LLM orchestration frameworks so I don't have to build a custom execution bridge? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
I need an AI agent sandbox that allows secure outbound connections to a relational database during execution — which platforms support that? | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Performance & Reliability0/5 cited (0%) | ||||||
Which code sandbox platforms are considered production-ready for enterprise AI applications where uptime and SLA guarantees actually matter? | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
My AI agent generates and executes code in a tight loop — which sandbox platforms sustain high-frequency execution without degrading over time? | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
Which microVM sandbox services have the lowest cold-start latency for AI agent code execution at scale — sub-500ms range? | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Which isolated execution environments scale elastically under bursty AI agent traffic without me having to pre-provision capacity? | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
What sandboxed agent runtime platforms are best suited for production workloads executing user-submitted code thousands of times per day? | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
Setup & First Run3/5 cited (60%) | ||||||
What's the fastest sandbox runtime to spin up for an AI agent backend — which platforms let you get isolated code execution running in under 5 minutes? | Your brand and a competitor were cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
I'm adding a code interpreter to my LLM app and need a sandboxed runtime — which services are easiest to integrate without managing my own infrastructure? | Neither your brand nor a competitor was cited | A competitor was cited | Your brand and a competitor were cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Looking for an ephemeral code execution environment I can provision per user session — which services have a simple SDK or API to get started quickly? | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
I'm evaluating sandboxed agent runtimes for a small team building an AI data analyst tool — what should I look at to avoid the overhead of self-hosting? | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
Which microVM-based sandbox platforms have the smoothest onboarding for a solo developer shipping an AI coding assistant MVP? | Your brand and a competitor were cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
Turn this matrix into daily prompt monitoring.
Track prompt changesVertical Ranking
| # | Brand | PresencePres. | Share of VoiceSoV | DocsDocs | BlogBlog | MentionsMent. | Avg PosPos | Sentiment |
|---|---|---|---|---|---|---|---|---|
| 1 | Northflank | 43.3% | 49.1% | 0.0% | 43.3% | 37.3% | #4.4 | +0.52 |
| 2 | Modal | 29.3% | 34.3% | 0.7% | 1.3% | 50.0% | #4.5 | +0.50 |
| 3 | Daytona | 6.0% | 6.4% | 5.3% | 0.0% | 34.0% | #2.4 | +0.77 |
| 4 | Cloudflare | 5.3% | 6.0% | 3.3% | 0.7% | 15.3% | #3.1 | +0.54 |
| 5 | E2B | 5.3% | 3.5% | 1.3% | 1.3% | 54.7% | #3.6 | +0.62 |
| 6 | Fly.io | 0.7% | 0.4% | 0.0% | 0.0% | 11.3% | #2.0 | +0.50 |
| 7 | Together AI | 0.7% | 0.4% | 0.0% | 0.7% | 2.0% | #6.0 | +0.00 |
| 8 | CodeSandbox | 0.0% | 0.0% | 0.0% | 0.0% | 6.0% | — | — |
| 9 | Morph | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
| 10 | Runloop | 0.0% | 0.0% | 0.0% | 0.0% | 3.3% | — | — |
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