E2B AI Sandboxes: Features, Applications & Real-World Impact

Author(s): Moein Moeinnia

Originally published on Towards AI.

Introduction: The AI Code Execution Challenge

Imagine building an AI assistant that can analyze data, generate visualizations, or write and execute code on the fly. Sounds powerful, right? But here’s the catch: how do you safely run AI-generated code without risking your entire infrastructure? One malicious line of code could compromise your system, leak sensitive data, or crash your servers.

This is where E2B (Execute to Build) comes in — a game-changing platform that lets AI agents and applications execute code in secure, isolated cloud environments. Think of it as giving your AI a safe playground where it can run wild without breaking anything important.

What Makes E2B Special?

1. Lightning-Fast Sandbox Startup

The Feature: E2B sandboxes spin up in approximately 150 milliseconds — faster than you can blink.

Why It Matters: Traditional virtual machines can take minutes to boot. E2B’s Firecracker-based microVMs start almost instantly, making real-time AI interactions possible. When your AI assistant needs to execute code to answer a user’s question, they won’t be left waiting.

Real-World Impact: Perplexity, the AI-powered search engine, uses E2B to provide advanced data analysis for their Pro users. The fast startup times mean users get instant results without frustrating delays.

2. Multi-Language Support

The Feature: Run Python, JavaScript, TypeScript, Ruby, C++, and more — all in the same platform.

Why It Matters: AI models don’t discriminate by programming language. GPT-4 might generate Python for data analysis, JavaScript for web scraping, or Bash scripts for system operations. E2B handles them all seamlessly.

Real-World Impact: Manus, an autonomous AI agent platform, leverages E2B’s multi-language support to use 27 different tools across various programming languages, enabling their agents to work like real humans across diverse tasks.

3. Enterprise-Grade Security

The Feature: Hardware-level isolation using Firecracker microVMs with KVM virtualization.

Why It Matters: When AI generates code, you can’t predict what it might do. E2B ensures that even if malicious code runs, it’s completely isolated from your infrastructure and other users’ sandboxes.

Security Layers:

• Hardware Isolation: Each sandbox runs in its own virtual machine with a dedicated kernel
• Minimal Attack Surface: Only essential virtual devices are exposed
• Jailer Process: Additional security barrier using cgroups and namespaces
• Network Controls: Configurable ingress/egress rules
• Resource Limits: CPU, memory, and storage constraints prevent resource exhaustion attacks

Real-World Impact: 88% of Fortune 100 companies trust E2B for their AI deployments — a testament to its enterprise-grade security posture.

4. Long-Running Sessions

The Feature: Sandboxes can run for up to 24 hours continuously.

Why It Matters: Not all AI tasks are quick. Training models, processing large datasets, or running complex simulations need time. E2B supports both quick executions (seconds) and marathon sessions (hours).

Real-World Impact: Research institutions use E2B for reinforcement learning experiments that require extended computation time, while maintaining isolation and security throughout.

5. Complete Environment Customization

The Feature: Create custom sandbox templates with pre-installed dependencies, libraries, and configurations.

Why It Matters: Every AI application has unique requirements. Data science projects need pandas and matplotlib. Web scraping needs Selenium. E2B lets you build Docker-based templates tailored to your exact needs.

How It Works:

# Initialize a custom template
e2b template init

# Customize your Dockerfile with dependencies
# Build and deploy
e2b template build
e2b template deploy

Real-World Impact: Groq, the AI inference company, uses customized E2B environments optimized for their high-speed code execution requirements.

6. File System Operations

The Feature: Full file system access with upload/download capabilities.

Why It Matters: AI agents often need to work with files — reading CSVs, generating reports, creating visualizations, or processing documents. E2B provides complete file system operations within each sandbox.

Capabilities:

• Upload files from your application to the sandbox
• Download generated files (charts, reports, processed data)
• Create, read, update, and delete files within the sandbox
• Persistent storage during the session lifetime

Real-World Impact: AI data analysts use E2B to upload datasets, process them with AI-generated Python code, and download beautiful visualizations — all in isolated environments.

7. Developer-Friendly SDKs

The Feature: Comprehensive Python and JavaScript/TypeScript SDKs with intuitive APIs.

Why It Matters: Complex infrastructure should be simple to use. E2B’s SDKs abstract away the complexity, letting developers focus on building AI features rather than managing infrastructure.

Python Example:

from e2b_code_interpreter import Sandbox

with Sandbox.create() as sandbox:
 # Upload data
 sandbox.upload_file("data.csv")
 
 # Run analysis
 execution = sandbox.run_code("""
 import pandas as pd
 import matplotlib.pyplot as plt
 
 df = pd.read_csv('data.csv')
 df.plot(kind='bar')
 plt.savefig('chart.png')
 """)
 
 # Download results
 chart = sandbox.download_file("chart.png")

JavaScript Example:

import { Sandbox } from '@e2b/code-interpreter'

const sandbox = await Sandbox.create()
await sandbox.uploadFile("data.csv")
const execution = await sandbox.runCode(`
 const fs = require('fs')
 const data = fs.readFileSync('data.csv', 'utf8')
 // Process data
`)
await sandbox.downloadFile("results.json")

8. Scalability & Auto-Scaling

The Feature: Run thousands of concurrent sandboxes with automatic resource provisioning.

Why It Matters: AI applications can experience sudden traffic spikes. E2B automatically scales to meet demand without manual intervention.

Real-World Impact: Hugging Face uses E2B to scale AI research with tens of thousands of concurrent machines, processing massive workloads for model evaluation and reinforcement learning.

9. Self-Hosting & On-Premises Deployment

The Feature: Deploy E2B in your own AWS/GCP account or on-premises infrastructure.

Why It Matters: Enterprises with strict data residency requirements or compliance needs can run E2B within their own security perimeter while maintaining full control.

Benefits:

• Complete data sovereignty
• Custom network configurations
• Integration with existing security tools
• Compliance with industry regulations (HIPAA, PCI-DSS)

10. Observability & Monitoring

The Feature: Comprehensive logging, metrics, and real-time monitoring of sandbox operations.

Why It Matters: In production, you need visibility into what’s happening. E2B provides detailed insights into code execution, resource usage, errors, and performance.

Monitoring Capabilities:

• Execution logs and stdout/stderr capture
• Resource utilization metrics (CPU, memory, disk)
• Execution time tracking
• Error and exception logging
• Audit trails for compliance

Where E2B Shines: Practical Applications

1. AI-Powered Data Analysis & Visualization

The Use Case: Build AI assistants that can analyze datasets, generate insights, and create visualizations on demand.

How E2B Helps:

• Safely execute pandas, numpy, and matplotlib code
• Process user-uploaded datasets in isolation
• Generate charts, graphs, and statistical reports
• Return results without exposing infrastructure

Who’s Using It: Perplexity implemented advanced data analysis for Pro users in just one week using E2B, allowing users to upload data and get AI-generated insights instantly.

2. AI Coding Assistants & Code Interpreters

The Use Case: Create AI tools that write, test, and execute code to solve programming problems.

How E2B Helps:

• Run AI-generated code across multiple languages
• Test code snippets safely before deployment
• Debug and iterate on solutions in real-time
• Provide interactive coding environments

Who’s Using It: Educational platforms use E2B to build AI tutors that generate code examples and execute them live, helping students learn programming concepts interactively.

3. Autonomous AI Agents

The Use Case: Build AI agents that can perform complex, multi-step tasks autonomously — from research to execution.

How E2B Helps:

• Provide full virtual computers for agents
• Enable terminal access and system-level operations
• Support long-running workflows (up to 24 hours)
• Isolate agent actions for security

Who’s Using It: Manus uses E2B to power autonomous agents that can create comprehensive marketing strategies, generate reports, and execute complex workflows using 27 different tools — all in secure virtual environments.

4. AI-Powered Web Scraping & Automation

The Use Case: Create AI agents that can browse the web, extract data, and automate online tasks.

How E2B Helps:

• Run browser automation tools (Selenium, Playwright)
• Execute web scraping scripts safely
• Process and transform scraped data
• Handle dynamic content and JavaScript rendering

Who’s Using It: Gumloop uses E2B for LLM-generated API integrations and web automation at scale, delivering reliable performance to their users.

5. AI Model Evaluation & Reinforcement Learning

The Use Case: Test, evaluate, and train AI models in controlled environments.

How E2B Helps:

• Provide isolated environments for model testing
• Support long-running training sessions
• Scale to thousands of concurrent evaluations
• Ensure reproducible results

Who’s Using It:

• Hugging Face: Uses E2B to replicate DeepSeek-R1 for code tests and reinforcement learning
• LMArena: Powers Web-Arena evaluations for AI model benchmarking, the most popular leaderboard for AI models

6. Interactive AI Playgrounds & Demos

The Use Case: Build interactive demos where users can experiment with AI-generated applications.

How E2B Helps:

• Run full-stack applications (Next.js, Streamlit, Gradio)
• Provide instant deployment of AI-generated apps
• Isolate user sessions for security
• Support public sharing and hosting

Who’s Using It: E2B’s open-source Fragments project (similar to Claude Artifacts) lets users build and deploy full applications generated by AI, hosted securely on E2B infrastructure.

7. Enterprise Workflow Automation

The Use Case: Automate complex business processes with AI agents that can execute code, access APIs, and process data.

How E2B Helps:

• Secure execution of workflow automation scripts
• Integration with enterprise systems via APIs
• Compliance with security and audit requirements
• Scalability for enterprise-wide deployments

Who’s Using It: Fortune 100 companies across industries use E2B to automate tasks ranging from data processing to report generation, with 88% adoption rate among Fortune 100 firms.

8. AI-Powered Research & Experimentation

The Use Case: Enable researchers to run experiments, test hypotheses, and analyze results using AI assistance.

How E2B Helps:

• Provide reproducible research environments
• Support scientific computing libraries
• Enable collaborative research with shared contexts
• Maintain security for sensitive research data

Who’s Using It: Research institutions use E2B for AI experiments, computational research, and academic projects requiring secure, scalable computing resources.

9. Code Generation & Testing Platforms

The Use Case: Build platforms that generate, test, and validate code automatically.

How E2B Helps:

• Execute generated code in safe environments
• Run automated tests and validations
• Provide immediate feedback on code quality
• Support continuous integration workflows

Who’s Using It: Developer tools and CI/CD platforms integrate E2B to safely execute user-submitted or AI-generated code during testing and validation phases.

10. AI-Powered Customer Support Tools

The Use Case: Create support assistants that can execute diagnostic scripts, analyze logs, and troubleshoot issues.

How E2B Helps:

• Run diagnostic and troubleshooting scripts
• Analyze system logs and error messages
• Generate solutions and test fixes
• Maintain security while accessing customer data

Why E2B is a Game-Changer

The Old Way: Risky and Slow

Before E2B, developers had limited options:

• Run code locally: Dangerous — one bad script could compromise your system
• Use traditional VMs: Slow startup times (minutes) and high resource overhead
• Use containers: Better, but still vulnerable to container escape attacks
• Build custom infrastructure: Expensive, time-consuming, and complex

The E2B Way: Safe, Fast, and Simple

• Security: Hardware-level isolation with Firecracker microVMs
• Speed: 150ms startup times for real-time interactions
• Simplicity: Simple SDK calls replace complex infrastructure management
• Scalability: Automatic scaling from one to thousands of sandboxes
• Flexibility: Complete customization and multi-language support

Getting Started: From Zero to Running Code in Minutes

Step 1: Sign Up and Get API Key

# Visit e2b.dev and create an account
# Get your API key from the dashboard
export E2B_API_KEY="your-api-key-here"

Step 2: Install SDK

# Python
pip install e2b-code-interpreter

# JavaScript/Node.js
npm install @e2b/code-interpreter

Step 3: Run Your First Sandbox

from e2b_code_interpreter import Sandbox

with Sandbox.create() as sandbox:
 execution = sandbox.run_code("print('Hello from E2B!')")
 print(execution.text) # Output: Hello from E2B!

That’s it! You’re now able to run AI-generated code in a secure cloud sandbox.

Real-World Success Stories

Perplexity: One Week to Production

Perplexity integrated E2B to add advanced data analysis features for their Pro users. The implementation took just one week with one engineer working in spare cycles. Users can now upload datasets and get AI-powered insights instantly.

Hugging Face: Scaling AI Research

Hugging Face uses E2B to scale their AI research operations, running tens of thousands of concurrent sandboxes for model evaluation, reinforcement learning, and code testing. The platform’s ability to handle massive scale with minimal setup time was crucial.

Manus: Autonomous Agents at Scale

Manus built a sophisticated multi-agent system using E2B that can generate 50+ page marketing strategies, create comprehensive reports, and execute complex workflows autonomously. The platform’s 24-hour session support and full virtual computer access were essential for their use case.

Gumloop: Reliable Performance at Scale

Gumloop uses E2B for LLM-generated API integrations and workflow automation. The platform’s reliability and performance enable them to deliver consistent results to their users, building enterprise trust in their AI-powered automation tools.

The Bottom Line: Why Choose E2B?

Choose E2B if you need:

• ✅ Secure execution of AI-generated code
• ✅ Fast startup times for real-time interactions
• ✅ Multi-language support (Python, JavaScript, Ruby, C++, etc.)
• ✅ Enterprise-grade security and compliance
• ✅ Scalability from prototype to production
• ✅ Simple developer experience with great SDKs
• ✅ Custom environments tailored to your needs
• ✅ Self-hosting options for data sovereignty
• ✅ Long-running sessions (up to 24 hours)
• ✅ Proven track record (88% of Fortune 100 companies)

E2B is perfect for:

• AI coding assistants and code interpreters
• Data analysis and visualization tools
• Autonomous AI agents and workflow automation
• AI-powered research and experimentation
• Educational platforms and AI tutors
• Web scraping and browser automation
• Model evaluation and reinforcement learning
• Interactive AI playgrounds and demos

Conclusion: The Future of AI Code Execution

E2B has fundamentally changed how we think about AI code execution. By combining Firecracker’s security with developer-friendly SDKs and enterprise-grade scalability, E2B has become the de facto standard for safe AI code execution — trusted by 88% of Fortune 100 companies and powering millions of sandbox sessions.

Whether you’re building the next AI-powered data analyst, an autonomous agent platform, or an interactive coding assistant, E2B provides the secure, fast, and scalable infrastructure you need to bring your vision to life.

The best part? You can get started in minutes, not months. No complex infrastructure to manage, no security nightmares to worry about — just simple, powerful APIs that let you focus on building amazing AI experiences.

Ready to give your AI the safe playground it deserves? Start building with E2B today.

Additional Resources

• Official Documentation: e2b.dev/docs
• GitHub Repository: github.com/e2b-dev/E2B
• Code Examples: E2B Cookbook

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Published via Towards AI