AI visibility report for E2B
Vertical: AI Code Sandboxes & Agent Runtimes
AI search visibility benchmark across 5 platforms in AI Code Sandboxes & Agent Runtimes.
Presence Rate
Top-3 citations across 125 prompt × platform pairs
Sentiment
Peer Ranking
Key Metrics
Platform Breakdown
Grok
ChatGPT
Google AI Mode
Gemini Search
PerplexityOverview
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
For production workloads that run user-submitted code thousands of times per day, the strongest fits are Northflank, Modal, E2B, Blaxel, and Daytona ; the best choice depends on whether you value isolation, unlimited runtime, GPU access, or easy self...
What sandboxed agent runtime platforms are best suited for production workloads executing user-submitted code thousands of times per day?
Sandboxes universal API (Cased) * What it is: A universal API that lets you target multiple providers through a single interface (e.g., E2B, Modal, Daytona, Cloudflare) with automatic provider selection.
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?
Other notable mentions * E2B, Blaxel, and similar platforms offer GPU-enabled sandboxes or enterprise-focused GPU access, often with stricter compliance or broader enterprise tooling.
I need a code execution environment that supports GPU workloads for AI-generated training scripts — which sandboxed platforms handle that use case?
Most cited sources8
- 77
E2B | The Enterprise AI Agent Cloud
e2b.dev·Landing Page
- 11
Documentation - E2B
e2b.dev·Landing Page
- 11
E2B Documentation
e2b.dev·Landing Page
8e2b-dev/E2B: Open-source, secure environment with real- ...
github.com·Documentation
- 7
Documentation - E2B
e2b.dev·Landing Page
- 7
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 Coverage
Prompt-Level Results
| Prompt | Google AI Mode | Gemini Search | Grok | ChatGPT | Perplexity |
|---|---|---|---|---|---|
Capability3/5 cited (60%) | |||||
Which sandboxed execution platforms let AI agents run arbitrary shell commands safely without kernel-level escape risks or shared-tenant interference? | |||||
What are the best isolated runtime options for AI agents that need persistent filesystem state across multiple execution steps in a single session? | |||||
Which agent runtime platforms support spawning concurrent sandbox instances so multiple AI agents can run code in parallel for a multi-agent workflow? | |||||
I need a code execution environment that supports GPU workloads for AI-generated training scripts — which sandboxed platforms handle that use case? | |||||
Looking for a sandboxed code interpreter that can handle long-running jobs — 10 to 30 minutes — without hitting timeout limits. What are my options? | |||||
Developer Experience2/5 cited (40%) | |||||
Which agent compute platforms have the most active developer communities and solid docs for teams just getting into agentic AI workflows? | |||||
I want a sandboxed runtime where my team can define reusable execution templates — which platforms make that workflow easy without deep infra knowledge? | |||||
Which code sandbox services have good observability built in so I can actually debug what my AI agent is running inside the environment? | |||||
What do platform engineers typically use to manage ephemeral execution environments for AI agents — and which options have the least operational burden? | |||||
Which AI sandbox platforms offer the best developer experience for iterating on agent tools locally before deploying to production? | |||||
Integrations & Ecosystem5/5 cited (100%) | |||||
What are the best code execution sandbox options that support pre-installing custom dependencies from a private package registry before agent runs? | |||||
Which sandboxed agent runtimes integrate well with popular LLM orchestration frameworks so I don't have to build a custom execution bridge? | |||||
What sandboxed execution environments have good support for streaming output back to the calling application in real time during an agent's code run? | |||||
Which agent compute platforms avoid heavy lock-in and work across major cloud providers so I can keep data residency in my existing infrastructure? | |||||
I need an AI agent sandbox that allows secure outbound connections to a relational database during execution — which platforms support that? | |||||
Performance & Reliability4/5 cited (80%) | |||||
What sandboxed agent runtime platforms are best suited for production workloads executing user-submitted code thousands of times per day? | |||||
Which code sandbox platforms are considered production-ready for enterprise AI applications where uptime and SLA guarantees actually matter? | |||||
Which microVM sandbox services have the lowest cold-start latency for AI agent code execution at scale — sub-500ms range? | |||||
My AI agent generates and executes code in a tight loop — which sandbox platforms sustain high-frequency execution without degrading over time? | |||||
Which isolated execution environments scale elastically under bursty AI agent traffic without me having to pre-provision capacity? | |||||
Setup & First Run3/5 cited (60%) | |||||
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? | |||||
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? | |||||
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? | |||||
Which microVM-based sandbox platforms have the smoothest onboarding for a solo developer shipping an AI coding assistant MVP? | |||||
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? | |||||
Strengths1
What are the best code execution sandbox options that support pre-installing custom dependencies from a private package registry before agent runs?
Avg # 3.0 · 3 platforms
Gaps5
What sandboxed agent runtime platforms are best suited for production workloads executing user-submitted code thousands of times per day?
Competitors on 4 platforms
Which code sandbox platforms are considered production-ready for enterprise AI applications where uptime and SLA guarantees actually matter?
Competitors on 4 platforms
My AI agent generates and executes code in a tight loop — which sandbox platforms sustain high-frequency execution without degrading over time?
Competitors on 4 platforms
I want a sandboxed runtime where my team can define reusable execution templates — which platforms make that workflow easy without deep infra knowledge?
Competitors on 3 platforms
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?
Competitors on 3 platforms
Vertical Ranking
| # | Brand | PresencePres. | Share of VoiceSoV | DocsDocs | BlogBlog | MentionsMent. | Avg PosPos | Sentiment |
|---|---|---|---|---|---|---|---|---|
| 1 | Northflank | 66.4% | 42.4% | 3.2% | 66.4% | 58.4% | #19.4 | +0.37 |
| 2 | Modal | 49.6% | 25.5% | 6.4% | 8.0% | 48.0% | #18.1 | +0.41 |
| 3 | E2B | 25.6% | 13.2% | 10.4% | 8.0% | 25.6% | #26.1 | +0.40 |
| 4 | Daytona | 15.2% | 7.3% | 7.2% | 3.2% | 15.2% | #18.9 | +0.46 |
| 5 | Cloudflare | 12.0% | 4.0% | 2.4% | 6.4% | 11.2% | #27.0 | +0.42 |
| 6 | Fly.io | 6.4% | 2.5% | 3.2% | 0.8% | 6.4% | #17.6 | +0.41 |
| 7 | CodeSandbox | 4.8% | 2.0% | 2.4% | 0.0% | 4.8% | #24.7 | +0.38 |
| 8 | Together AI | 4.0% | 0.9% | 0.0% | 2.4% | 4.0% | #7.3 | +0.42 |
| 9 | Runloop | 4.0% | 2.2% | 2.4% | 0.0% | 4.0% | #62.7 | +0.40 |
| 10 | Morph Labs | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
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