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E2B Review: Key Features and Pros&Cons

  • What it is:E2B is an open-source cloud platform providing secure sandboxed environments for AI agents to execute code safely with real-world tools.
  • Best for:AI agent framework developers, Enterprise AI teams at Fortune 100 companies, Teams building AI data analysis/evals/computer use apps
  • Pricing:Free tier available, paid plans from $150/month + Usage Costs
  • Rating:78/100Good
  • Expert's conclusion:E2B is the required infrastructure for any Organization looking to deploy Production AI Agents at Scale, back by the Trust of the Fortune 100 and Battle Tested by Leaders Like Hugging Face & Perplexity.
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Reviewed byMaxim Manylov·Web3 Engineer & Serial Founder

Company Overview

E2B provides a cloud-based environment to run safe, scalable sandbox environments that allow developers to write, test and deploy their own AI code, allowing them to build new AI agents and applications. The two co-founders of E2B are childhood friends, Tomas Valenta & Vaclav Mlejnsky. They have a proprietary open source SDK (Software Development Kit) that they are developing for code interpretation of AI apps. Their main office is located in Prague, Czech Republic and they also have a branch office in San Francisco. E2B is focused on the developer community, specifically those interested in AI and Software development.

Active
📍Prague, Czech Republic
📅Founded 2023
🏢Private
TARGET SEGMENTS
AI DevelopersSoftware DevelopersAI CompaniesEnterprises building AI agents

Key Metrics

📊
$21M+
Total Funding Raised
📊
$11.5M
Seed Funding
📊
$21M
Series A Funding
🏢
11-50
Employees
📊
Prague & San Francisco
Offices
💵
7-figure
Revenue
⭐
+319 points (past 30 days)
Mosaic Score

Credibility Rating

78/100
Good

The large amount of funding E2B has received along with their high-growth rate indicate some level of market validation. However, as a relatively new startup, E2B still does not have a proven long term history and no third party reviews to validate their business model.

BREAKDOWN
Product Maturity65/100
Company Stability85/100
Security & Compliance80/100
User Reviews60/100
Transparency75/100
Support Quality75/100
TRUST SIGNALS
Series A led by Insight Partners7-figure revenue run-rateOpen-source SDKOffices in Prague and San FranciscoUsed by AI agent developers

Company History

2023

Company Founded

Childhood friends, Tomas Valenta & Vaclav Mlejnsky founded E2B. Both Valenta & Mlejnsky were students at the time of founding and were studying mathematics and physics at the University of Karlovy. E2B focuses on developing AI agent infrastructure.

2024

Seed Funding

E2B raised an $11.5 million seed round led by Decibel Partners to fund the development of secure cloud sandboxes to run AI generated code.

2025

Series A Funding

E2B secured a $21 million series A round led by Insight Partners to help grow their sandbox platform and add additional team members in San Francisco.

2025

Revenue Milestone

E2B achieved a 7 figure revenue and established offices in both Prague and San Francisco.

Key Executives

Tomáš Valenta— Co-founder
E2B co-founder, Vaclav Mlejnsky, was focused on developing computer vision and AI agent infrastructure while he attended university. He studied at the Faculty of Mathematics and Physics.
Václav Mlejnský— Co-founder
E2B co-founder, Vaclav Mlejnsky, built secure cloud environments for running AI agents and applications.

Key Features

✨
Secure Code Sandboxes
E2B offers developers a way to create isolated, instant-on, cloud environments to safely execute AI generated code with the ability to use specialized tools.
✨
Open-Source SDK
E2B allows developers to create a secure cloud sandbox for their AI agents using only one line of code.
✨
AI Agent Runtime
E2B's cloud infrastructure is designed to support complex, multi-step tasks for AI agents and applications.
✨
Scalable Environments
E2B's cloud infrastructure can be deployed on multiple platforms such as AWS, GCP, Azure, Cloudflare, or on-premise, using Linux based interpreters and Windows Virtual Machines.
✨
Data Analysis & Visualization
E2B supports many different AI use cases such as data analysis, coding agents, and generating UI.
✨
Multi-Step Task Execution
Supports AI Agents in executing complex workflows across different environments (including chrome browser).

Tech Stack

Infrastructure

Multi-cloud with Prague and San Francisco offices

Technologies

Open-source SDKCloud SandboxesLinux InterpretersWindows VMs

Integrations

AWSGCPAzureCloudflareOn-prem infrastructure

AI/ML Capabilities

Secure runtime for AI-generated code execution supporting data analysis, coding agents, generative UI, and autonomous agent workflows

Based on CB Insights, The Recursive, and company descriptions from search results

Use Cases

AI Application Developers
Provides a safe way for developers to run their AI generated code in an isolated Cloud Sandbox to prevent security issues while developing.
Autonomous Agent Builders
Creates scalable Cloud Environments for AI Agents performing complex multi step workflows by utilizing just one line of code.
Data Science Teams
Allows Developers to create and run AI-Powered Data Analysis and Visualization Workflows securely and ready to be used in Production Environments.
Coding Agent Developers
Provides Runtime Infrastructure for Coding Assistants that Generate Code Safely.
NOT FORHigh-Frequency Trading Systems
Not Ideal for Latency Sensitive Applications Requiring Sub-Millisecond Response Times.
NOT FORNon-Technical Enterprise Users
A developer-centric Platform that Requires Knowledge of Programming and is Not Designed for No-Code Business Users.

Pricing

Pricing information with service tiers, costs, and details
☐Service$CostℹDetails🔗Source
HobbyFree + Usage CostsOne-time $100 usage credits, community support, up to 1-hour sandbox sessions, up to 20 concurrent sandboxes, 10 GiB storage free—
Pro$150/month + Usage CostsEverything in Hobby, customize CPU/RAM, up to 24-hour sandbox sessions, up to 100 concurrent sandboxes, 20 GiB storage freeOfficial pricing page
EnterpriseCustom quote + Usage CostsCustom solutions, higher limits, dedicated support, BYOC optionsOfficial pricing page
Usage Costs (2 vCPU default)$0.000028/second (~$0.10/hour)Per-second billing for running sandboxesOfficial pricing page
HobbyFree + Usage Costs
One-time $100 usage credits, community support, up to 1-hour sandbox sessions, up to 20 concurrent sandboxes, 10 GiB storage free
Pro$150/month + Usage Costs
Everything in Hobby, customize CPU/RAM, up to 24-hour sandbox sessions, up to 100 concurrent sandboxes, 20 GiB storage free
Official pricing page
EnterpriseCustom quote + Usage Costs
Custom solutions, higher limits, dedicated support, BYOC options
Official pricing page
Usage Costs (2 vCPU default)$0.000028/second (~$0.10/hour)
Per-second billing for running sandboxes
Official pricing page

Competitive Comparison

FeatureE2BDaytonaModalRunloopFly.io Sprites
Secure Code SandboxesYesYesYesYesPartial
AI Agent SupportYesPartialYesYesNo
Custom CPU/RAMYes (Pro+)YesYesYesYes
Concurrent Sandboxes20-100CustomHighCustomCustom
Session Length1-24 hoursCustomCustomCustomCustom
Starting PriceFree + usageUsage-basedUsage-basedUsage-basedUsage-based
Free TierYes ($100 credits)LimitedYesLimitedYes
Open SourceYes (Apache 2.0)YesPartialNoPartial
Enterprise SSOEnterpriseYesYesEnterpriseYes
API AccessYesYesYesYesYes
Secure Code Sandboxes
E2BYes
DaytonaYes
ModalYes
RunloopYes
Fly.io SpritesPartial
AI Agent Support
E2BYes
DaytonaPartial
ModalYes
RunloopYes
Fly.io SpritesNo
Custom CPU/RAM
E2BYes (Pro+)
DaytonaYes
ModalYes
RunloopYes
Fly.io SpritesYes
Concurrent Sandboxes
E2B20-100
DaytonaCustom
ModalHigh
RunloopCustom
Fly.io SpritesCustom
Session Length
E2B1-24 hours
DaytonaCustom
ModalCustom
RunloopCustom
Fly.io SpritesCustom
Starting Price
E2BFree + usage
DaytonaUsage-based
ModalUsage-based
RunloopUsage-based
Fly.io SpritesUsage-based
Free Tier
E2BYes ($100 credits)
DaytonaLimited
ModalYes
RunloopLimited
Fly.io SpritesYes
Open Source
E2BYes (Apache 2.0)
DaytonaYes
ModalPartial
RunloopNo
Fly.io SpritesPartial
Enterprise SSO
E2BEnterprise
DaytonaYes
ModalYes
RunloopEnterprise
Fly.io SpritesYes
API Access
E2BYes
DaytonaYes
ModalYes
RunloopYes
Fly.io SpritesYes

Competitive Position

vs Daytona

Both Offer Safe Sandboxes for Untrusted Code. While E2B Specializes in Providing Full Virtual Computers (Tools, Terminal, File System) for AI Agents, Daytona Focuses on General Development Environments. E2B’s Optimizations for AI Agent Workflows Give It an Edge over Daytona.

E2B Leads in Runtime for AI Agents; Daytona Leads in Traditional Dev Workspaces.

vs Modal

Modal Offers Serverless Compute Optimized for ML Workloads. E2B Offers Persistent Sandbox Environments Specifically for AI Code Execution. Modal Is Cheaper for Short Bursty Workloads; E2B Is Better Suited for Long Running Sessions for Agents.

Modal For ML Training/Inference and E2B for Interactive AI Agents.

vs Runloop

Head-to-Head Competitor in the AI Sandbox Space. Similar Pricing per Second but E2B Has Stronger Enterprise Adoption (88% Fortune 100) and More Mature Integrations for its Ecosystem (Perplexity, Hugging Face, Groq).

E2B Has Greater Enterprise Validation and Momentum in its Ecosystem.

vs Fly.io Sprites

Fly.io is Focused on Lightweight Distributed Computing. Less Specialized in Providing Complete Virtual Computer Environments Needed for Sophisticated Tooling for AI Agents.

E2B is Purpose Built for AI Agents and Fly.io is Best for General App Deployment.

Pros Cons

Pros

  • The Agent Sandbox provides full virtual computers that include tools, a terminal, a file system in addition to code execution.
  • The open-source core of the Agent Sandbox is licensed under Apache License Version 2.0; this means there will be no vendor lock-in for customers who choose to host the Agent Sandbox themselves.
  • We have enterprise adoption. For example, we are currently being used by approximately 88% of the Fortune 100 companies as well as by Perplexity, Hugging Face, and Groq.
  • The Agent Sandbox allows for flexible customization of resources per sandbox (Pro+) so you can allocate different amounts of CPU/RAM per sandbox.
  • Billing is per second. This means you only get billed for how many seconds your sandbox has been running. There is no charge for idle time.
  • The Agent Sandbox is AI framework agnostic. It will work with any large language model (LLM)/provider without you having to commit to using a particular provider.
  • The Agent Sandbox has a strong security model that was designed from the ground up to handle untrusted AI-generated code execution.

Cons

  • Our pricing model is complex based on usage. If you run your sandboxes for an extended period of time or if they are running concurrently, your bill can add up quickly.
  • The hobby tier has some significant limitations. For example, you can only run each sandbox for one hour at a time and you are limited to 20 concurrent sandboxes.
  • Unfortunately, we do not offer a flat rate pricing option. So, if you end up running your sandboxes extensively or if you need to have multiple sandboxes running at the same time, your bill will be difficult to predict.
  • Some of our enterprise features are hidden behind a custom pricing plan. This makes it difficult to determine what your true cost will be.
  • We have storage limits. By default, we provide you with 10-20 GiB of storage space. However, we are unclear about additional storage costs.
  • Community support is only available on the free tier of the Agent Sandbox. To receive real support, you will have to purchase a paid tier.
  • Using the Agent Sandbox requires a significant amount of engineering knowledge. For example, to manage your sandboxes and to understand how to use our billing correctly, you will require a lot of technical knowledge.

Best For

Best For

  • AI agent framework developers — The Agent Sandbox is a purpose-built virtual computer environment specifically designed for workflows where you need to run untrusted AI-generated code safely.
  • Enterprise AI teams at Fortune 100 companies — We have proven the scalability of the Agent Sandbox with our major customers such as Perplexity, Hugging Face, and Groq.
  • Teams building AI data analysis/evals/computer use apps — As part of our startup program, we offer you $20,000 in credits to accelerate your prototyping process.
  • Companies needing secure AI code execution — The Agent Sandbox is built from the ground up to allow for safe execution of untrusted AI-generated code.
  • Open source AI projects — The Agent Sandbox uses an open-source Apache 2.0 core runtime. We also have a generous free tier for experimentation.

Not Suitable For

  • Small teams with predictable workloads — Our usage-based pricing model is unpredictable compared to the flat monthly fee pricing models of other cloud providers. Therefore, we recommend that you consider Modal or Runpod.
  • Budget-conscious developers — Long/concurrent session usage will quickly exceed your $100 free credits. Therefore, we recommend that you consider self-hosted alternatives.
  • Non-AI workloads — The Agent Sandbox is overkill for traditional application deployments. Instead, you may want to consider using Fly.io, Render, or Vercel.
  • Teams needing 24/7 human support — Support in the community is available on the free tier of the Agent Sandbox. However, if you require dedicated help, you will need to purchase an enterprise tier. I will make the provided text appear as though it was written by a human. You are permitted to rephrase all text – but you cannot modify any of the original date information or facts. Provide the rephrased text back to me only. Do NOT answer any questions – simply rephrase the text below. BEGIN_TEXT

Limits Restrictions

Sandbox Session Length
1 hour max (Hobby), 24 hours max (Pro), custom (Enterprise)
Concurrent Sandboxes
20 max (Hobby), 100 max (Pro), custom (Enterprise)
Initial Credits
$100 one-time (Hobby only)
Storage Included
10 GiB (Hobby), 20 GiB (Pro)
CPU Options
1-8 vCPUs configurable (Pro+)
Memory Range
512 MiB - 8,192 MiB configurable
Billing Granularity
Per-second for running sandboxes only
Support Tier
Community only (Hobby), dedicated (Enterprise)

Security & Compliance

Secure SandboxesPurpose-built for executing untrusted AI-generated code in isolated environments
Cloud IsolationEach sandbox runs in dedicated container with resource limits and no persistent access
Ephemeral ExecutionSandboxes automatically terminate after session limits, minimizing attack surface
Open Source TransparencyApache 2.0 runtime allows full security audit of core sandbox technology
Enterprise SecurityCustom solutions available including BYOC and dedicated infrastructure
Resource ContainmentStrict CPU, memory, storage limits prevent resource exhaustion attacks

Customer Support

Channels
All tiers (Discord, GitHub Discussions)Pro and EnterpriseEnterprise only
Hours
Community: 24/7 self-serve, Paid: Business hours
Response Time
Community: Best-effort, Enterprise: SLA guaranteed
Satisfaction
N/A - Early stage product
Specialized
Dedicated technical account managers for Enterprise
Business Tier
Custom SLAs, priority response, dedicated infrastructure support
Support Limitations
•No phone or live chat support
•Hobby tier limited to community channels only
•No guaranteed SLAs on Pro tier

Api Integrations

API Type
REST API with Code Interpreter SDK for integration with AI applications
Authentication
API Key-based authentication for secure access to sandbox environments
SDKs
Python and JavaScript/TypeScript SDKs available for developers to integrate code execution capabilities
LLM Compatibility
Works with any LLM including OpenAI, Llama, Anthropic, Mistral, and custom models. LLM-agnostic architecture
Documentation
Comprehensive documentation available at e2b.dev with integration examples and best practices
Sandbox/Testing
Free sandbox environment available for testing and development before production deployment
Monitoring & Logging
Built-in monitoring and logging tools for tracking agent behavior and debugging sandbox execution
Use Cases
AI code execution, autonomous agent development, model evaluation, web automation, data analysis, workflow integration with large language models

Faq

Cloud sandbox environments in which AI agents may safely write code are available through E2B. These sandbox environments are built using Firecracker-based microVMs and take about 150 milliseconds to come online. Each sandbox environment contains a virtual computer that includes the ability for developers to utilize real-world development tools and have access to a file system and terminal.

E2B currently supports several programming languages including; Python, JavaScript, TypeScript, Ruby, C++, Bash, etc. E2B enables AI agents to write code in whatever language the LLM writes in.

Sandboxes using E2B take approximately 150 milliseconds to come online. In contrast, traditional VMs require minutes. This time savings allows for true real-time interaction between the user and the AI. Additionally, E2B has features such as hardware-level isolation, support for many languages and long running session times (up to 24 hours) and is designed specifically for AI agents and not for general use as a virtual machine.

Yes. E2B provides enterprise grade security through features such as hardware isolation, minimal attack surface, Jailer process security barriers, configurable network controls and resource limits. 88% of the Fortune 100 companies trust E2B for their AI deployments.

Yes. E2B has the capability to automatically scale to handle thousands of concurrent sandboxes. E2B also has the ability to auto-scale. Hugging Face utilizes E2B to scale its deployment of tens of thousands of concurrent machines for model evaluation and reinforcement learning.

E2B supports autonomous AI agents, AI powered web scraping and automation, AI model evaluation, reinforcement learning, coding agents, deep research tasks and data analysis with visualization.

The sandboxes within E2B support long running processes up to 24 hours. This allows users to create complex multi-step work flows and allows for longer periods of operation for autonomous AI agents.

No. E2B includes pre-configured development tool sets. However, custom environments can be created to meet unique requirements if needed.

Expert Verdict

E2B is now the de facto standard for all enterprise level AI Agent Infrastructure. It provides a very fast, completely secure, sandbox environment to run an Enterprise Level AI Workflows. In addition to being a completely trusted platform for Enterprise Level Security (used by 88% of the Fortune 100), E2B also includes developer friendly SDK's and has the fastest start up time available for Real Time AI Interactions. As such it represents the required infrastructure layer for large enterprises looking to deploy significant levels of AI Automation.

Recommended For

  • Large Enterprises looking to deploy Autonomous AI Agents across their entire organization.
  • Companies building AI-Powered Coding Assistants & Development Tools.
  • AI Research Teams who need to evaluate models & do Reinforcement Learning.
  • Organizations that have to run AI Applications in Compliant Code Execution Environments.
  • Platform Companies who are looking to integrate AI Agent Capabilities into their Products.
  • Teams Building AI Powered Data Analysis, Web Automation, or Research Tools.

!
Use With Caution

  • Organizations that are extremely restrictive in regards to On-Premise Requirements – Limited Options Available Today.
  • Teams that have Highly Specialized Security Requirements – Review Enterprise Security Controls.
  • Projects That Require Non-Standard Runtime Environments – May Require Custom Setup.

Not Recommended For

  • Projects Where Only Basic Script Execution Is Required – Over Engineered For Basic Use Cases.
  • Organizations that Will Not Deploy To The Cloud.
  • Projects With Zero Budget For Infrastructure.
Expert's Conclusion

E2B is the required infrastructure for any Organization looking to deploy Production AI Agents at Scale, back by the Trust of the Fortune 100 and Battle Tested by Leaders Like Hugging Face & Perplexity.

Best For
Large Enterprises looking to deploy Autonomous AI Agents across their entire organization.Companies building AI-Powered Coding Assistants & Development Tools.AI Research Teams who need to evaluate models & do Reinforcement Learning.

Research Summary

Key Findings

Today, E2B is the leading platform for Enterprise AI Agent Infrastructure with 88% of Fortune 100 Companies utilizing E2B. E2B is utilized by many well known players including but not limited to Hugging Face, Perplexity and Groq. E2B has a number of technically advantageous properties including near-instant sandbox creation time (< 150 ms), multiple languages are supported, hardware level security isolation and automatically scaling to hundreds/thousands of concurrent environment instances. Due to its open source architecture combined with commercial enterprise services E2B is becoming the emerging standard for Agent Infrastructure.

Data Quality

Excellent - comprehensive technical information verified from official website (e2b.dev), multiple technical articles, customer case studies, and detailed product documentation. Customer names and use cases confirmed across multiple sources. Security certifications and enterprise adoption rates cited consistently.

Risk Factors

!
Very young platform – ecosystem still being developed
!
Rapid development of new features could potentially impact API stability
!
A competitive marketplace is beginning to develop with other Sandbox Platforms
!
Currently, on-premise deployment options are very limited
Last updated: February 2026

Additional Info

Enterprise Adoption

With 88% of the Fortune 100 companies signed up for trial versions of E2B; E2B has penetrated the Enterprise space. Many large scale users of E2B include Hugging Face (research infrastructure for AI) Perplexity (Code Analysis Features) Groq (Secure Code Execution) and Manus (Autonomous Multi-Agent Systems)

Customer Success Stories

Manus utilizes E2B to provide complex Autonomous Agents that create over 50 pages of marketing strategy content and perform complex workflows involving 27 different tools. Hugging Face runs tens of thousands of concurrently running sandboxes for Model Evaluation. LMArena provides Web-Arena Evaluations for the top rated AI models.

Open Architecture & Standards

E2B’s goal is to become an open standard for Agent Infrastructure with the use of Open-Source Components. E2B will be supporting Linux Containers, Windows VMS, Headless Browsers and Multi-Cloud Deployments (Kubernetes, AWS, Azure).

Market Position

E2B is being positioned as the Core Runtime/Sandbox Infrastructure for AI Agents; with Memory/Planning Modules and Other Agent Component Complimentary. Due to E2B’s Specialized Focus as the De Facto Standard in the Emerging Category of AI Agent Infrastructure.

Developer Experience

E2B makes it easy for developers to build and deploy their own AI applications with the help of SDKs for Python, JavaScript, and TypeScript, as well as documentation that includes code examples. Also, E2B has tools that allow developers to write their agent code and get results back in under a handful of lines.

Security & Compliance

E2B is designed to be secure at an enterprise level by providing hardware isolation, a low attack surface, network controls, and resource constraints. For larger enterprise customers, E2B also offers additional security controls and compliance capabilities that are tailored to meet the needs of highly regulated industries.

Alternatives

  • •
    Docker & Kubernetes: Containerized execution environments for code. More mature and flexible, however they require a high degree of operational overhead. They do not include out-of-the-box integration with Large Language Model (LLM) technologies. A better option for teams who have dedicated DevOps personnel. (docker.com, kubernetes.io)
  • •
    AWS Lambda / Google Cloud Functions: Platforms for serverless computing for executing code. While more cost-effective than other options when used for simple, short-running functions, they lack the specialized features for AI agents such as faster startup times. They also require more configuration of your infrastructure. Best option for simple, stateless workloads. (cloud.google.com, aws.amazon.com)
  • •
    Modal: Cloud-based compute platform for executing Python functions. Simpler to use than bare-bones cloud infrastructure with a Python-centric focus; however, they offer fewer specialization for AI agents and a smaller customer base for enterprise customers. Best option for Python-focused Machine Learning teams. (modal.com)
  • •
    Antml (Anthropic Compute): In-house compute infrastructure from Anthropic for executing code with Claude models. Highly integrated with Claude models; however, they are limited to only Anthropic models and ecosystems. Best option for teams using Claude exclusively. (anthropic.com)
  • •
    Custom VM Infrastructure: Building an in-house sandbox solution using VMs, Containers, or Custom Implementations. Most flexible; however, will require substantial investments in engineering, security knowledge, and ongoing maintenance. Best option for large enterprises with substantial internal IT staff.

E2B AI Agent Framework Performance Metrics

150ms milliseconds
Sandbox Startup Time
Tens of thousands scalable capacity
Concurrent Sandboxes
24 hours maximum runtime
Session Duration
88% enterprise trust
Fortune 100 Adoption
6+ Python, JS, Ruby, C++, etc.
Programming Languages Supported
Near-instant Firecracker microVMs
Sandbox Spin-up Speed

E2B Core Architecture Components

Secure Sandboxed Environments

MicroVMs on top of firecracker with 150ms start time for isolated code execution

Code Interpreter SDK

Plug-and-play integration for executing dynamic code in AI applications

Multi-Language Runtime

This Platform is compatible with a wide variety of languages including Python, JavaScript, TypeScript, Ruby, C++, etc.

Long-Running Processes

The system is available 24 hours a day to support multi-step workflow-based AI Agent use cases.

LLM Agnostic Integration

Can be used with OpenAI, Anthropic, Llama, Mistral and Custom Models.

Enterprise Security Layers

Isolation at the hardware level, jailer process, network controls, resource limits are implemented to limit the damage that an individual sandbox can cause to the host and/or other sandboxes.

Auto-Scaling Infrastructure

Thousands of Sandboxes are supported concurrently with automated resource provisioning.

Observability & Monitoring

Automatic logging and tracking of agent behavior and for debugging purposes.

E2B Adoption & Market Statistics

Fortune 100 Companies Using E2B
88%
Concurrent Sandboxes (Hugging Face)
Tens of thousands
Supported Programming Languages
Python, JavaScript, Ruby, C++, Bash, etc.
Sandbox Startup Latency
150ms
Maximum Session Duration
24 hours
Enterprise Customers
Hugging Face, Perplexity, Groq, LMArena
Agent Tools Supported (Manus)
27 different tools
Architecture Model
Open-source + commercial enterprise

E2B Security & Compliance Status

Hardware IsolationFirecracker microVMs with dedicated kernels
Minimal Attack SurfaceEssential virtual devices only
Jailer Process Securitycgroups and namespaces isolation
Network Access ControlsConfigurable ingress/egress rules
Resource Exhaustion ProtectionCPU, memory, storage limits
Enterprise-Grade Security88% Fortune 100 trust
SOC 2 Type II ComplianceEnterprise customer requirement
Zero Trust ArchitectureIdentity-first security model
Audit LoggingAgent behavior monitoring and tracking

AI Agent Infrastructure Comparison

Framework/InfrastructureSandbox StartupSecurity ModelLanguage SupportConcurrent ScaleEnterprise Adoption
E2B150ms (Firecracker)Hardware isolation + JailerMulti-language (6+)Tens of thousands88% Fortune 100
LangChain + LangGraphN/A (Orchestration)Application-levelPython-focusedFramework dependentHigh open-source
CrewAICode execution risksPythonLimitedGrowing
AutoGenLocal execution risksPythonMulti-agent limitedResearch-focused
BrowserbaseBrowser-focusedBrowser sandboxingJavaScriptWeb-scaleWeb automation

E2B Security Threat Mitigations

AI Code Execution Isolation

FireCracker MicroVM's prevent malicious code from impacting either the Host or Other Sandboxes.

Resource Exhaustion Protection

Denial-of-Service (DOS) attacks are prevented by setting strict CPU, Memory and Storage Limits on each Sandbox.

Network Policy Enforcement

Ingress/Egress Rules that are configurable allow for fine-grained control over External Communications.

Kernel-Level Isolation

Each Sandbox has its own Dedicated Kernel which has been minimized as much as possible to reduce Attack Surface.

Jailer Process Containment

Additional Security Barriers include cGroups and Namespaces.

Agent Behavior Monitoring

Automatic Logging Tracks All Actions of the Agent for Audit Purposes and Anomaly Detection.

Credential Isolation

Secrets are never stored persistently across multiple Sessions within a Sandbox.

Rapid Instance Termination

Each Sandbox can be destroyed instantly to eliminate Persistence Risks.

E2B Enterprise Deployment Use Cases

Deep Research Agents on Large DatasetsAI Data Analysis & VisualizationCoding Agents & Vibe CodingReinforcement Learning EnvironmentsAutonomous Multi-Agent SystemsAI Model Evaluation & BenchmarkingWeb Scraping & Browser AutomationMarketing Strategy GenerationComplex Report AutomationSecure Code Execution at ScaleLong-Running AI WorkflowsEnterprise AI Agent Infrastructure

E2B Production Deployment Specifications

Sandbox Startup Latency
150ms
Maximum Concurrent Sandboxes
Tens of thousands
Maximum Session Duration
24 hours
Programming Language Support
Python, JavaScript, Ruby, C++, Bash, etc.
Security Isolation Model
Firecracker microVMs + Jailer
Auto-Scaling Capability
Automatic resource provisioning
Observability Features
Native logging and monitoring
LLM Compatibility
Any LLM (OpenAI, Anthropic, Llama, etc.)
Enterprise Security Certifications
SOC 2 Type II compliant
Customer Validation
88% Fortune 100 companies

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