Integrating E2B and Daytona: Cloud Sandboxes for Next-Gen AI Development

Picture this: You’re building an AI agent that can write and execute code on the fly. Sounds amazing, right? But then reality hits—what if that code is malicious or just buggy? How do you run it safely without compromising your entire system?

As someone who’s wrestled with these exact challenges in AI development, I can tell you that secure code execution isn’t just a nice-to-have—it’s essential. The rise of AI agents and dynamic code generation has fundamentally changed how we approach this problem. In 2025, two platforms have emerged as frontrunners: E2B and Daytona. They both tackle the same core issue—running untrusted code safely—but they do it in remarkably different ways.

Understanding their differences, integration approaches, and real-world applications isn’t just academic. It’s crucial for any developer building AI-powered applications that need to execute code securely. Let’s dive in and explore what makes these platforms tick.

Understanding the Landscape

The challenge of securely executing AI-generated code isn’t new, but the scale and urgency have exploded in recent years. Traditional approaches—sandboxing in containers or restricting execution—often force you to choose between security and performance. It’s like trying to have your cake and eat it too, but ending up with neither.

This is where E2B and Daytona step in, each offering a different philosophy on solving this thorny problem. Think of E2B as the battle-hardened veteran, built for enterprise-scale operations, while Daytona is the nimble startup, optimized for developer productivity. Both are pushing the boundaries of what’s possible with AI code execution, and understanding their approaches will help you choose the right tool for your project.

What is E2B?

E2B is an open-source cloud infrastructure platform built on Firecracker microVMs that lets you run AI-generated code in isolated, secure environments. Trusted by 88% of Fortune 100 companies, E2B has become the go-to solution for organizations needing enterprise-grade code execution capabilities.

What makes E2B special? It’s not just another sandbox—it’s battle-tested at massive scale. Companies like Perplexity and Hugging Face rely on it for their most demanding workloads. The platform provides SDK support for both Python and JavaScript, making it accessible whether you’re a backend engineer or a full-stack developer. And here’s the kicker: E2B handles all the heavy lifting of VM management, scaling, and security, so you can focus on what matters—building your application.

Recently, E2B has expanded its capabilities with MCP (Model Context Protocol) support in partnership with Docker, and they’ve even raised $21M in Series A funding, signaling strong confidence in their vision for AI infrastructure.

What is Daytona?

Daytona takes a different approach, positioning itself as infrastructure specifically built for AI agents. After pivoting in February 2025 from a development environment platform, Daytona now focuses on providing lightweight, stateful sandboxes optimized for running AI-generated code. Daytona uses standard Docker containers by default but supports enhanced isolation through Kata Containers and Sysbox when needed.

What sets Daytona apart is its developer-first mentality. Imagine spinning up a sandbox in just 90ms—that’s faster than you can blink! The platform emphasizes developer experience, offering rapid environment creation and built-in support for Git operations, file system management, and Language Server Protocol (LSP) features. It’s like having a full development environment that you can summon instantly.

Daytona has also introduced computer use sandboxes, allowing AI agents to interact with virtual desktops on Linux, Windows, or macOS. This opens up possibilities for agents that need to perform complex UI interactions or run desktop applications. If you’re building AI agents that need to “see” and interact with graphical interfaces, Daytona’s computer use feature is a game-changer.

Key Architectural Differences

When it comes to architecture, E2B and Daytona couldn’t be more different. It’s like comparing a fortress to a speedboat—both get you where you need to go, but they do it in completely different ways. Let’s break down what makes each approach unique and why it matters for your AI projects.

E2B’s Firecracker-Based Approach

E2B’s architecture revolves around Firecracker, a KVM-based microVM runtime developed by AWS. Each sandbox runs as a lightweight virtual machine with its own isolated kernel, providing true hardware-level isolation. This approach offers several benefits:

• Superior isolation: Hardware-level virtualization means malicious code in one sandbox cannot escape to affect the host or other sandboxes
• Fast cold starts: Despite being VMs, Firecracker’s minimal device model and optimized boot path enable sandbox creation in under 200 milliseconds
• Pre-warmed snapshots: E2B uses a sophisticated template-based system with snapshot/restore capabilities for even faster subsequent launches

If you’ve ever worried about a rogue AI script taking down your entire system, E2B’s approach gives you peace of mind. It’s the choice when security isn’t just important—it’s non-negotiable.

Daytona’s Container-First Approach

Daytona’s default configuration uses standard Docker containers, which offer familiarity and flexibility for most developers. However, this choice comes with trade-offs:

• Lighter weight: Containers are less resource-intensive than microVMs
• Configuration flexibility: Teams can enhance isolation through Kata Containers or Sysbox for specific use cases
• Development-friendly: Containers integrate seamlessly with existing Docker-based workflows
• Environmental creation speed: Daytona’s optimized container startup achieves 90ms environment creation

The trade-off is that default container-based isolation, while suitable for most AI agent scenarios, doesn’t provide the same level of security guarantees as microVM isolation. If you’re prototyping or working with trusted code, this might not matter. But if you’re executing code from unverified sources, it’s worth considering the security implications.

Think of it this way: E2B gives you a bank vault, while Daytona gives you a locked drawer. Both keep things secure, but one is clearly built for Fort Knox-level protection.

Integration Workflow in Practice

Enough theory—let’s see how these platforms work in the real world. Getting started with either E2B or Daytona is surprisingly straightforward. You’ll be up and running in minutes, not hours. Here’s what the integration looks like for each platform.

Getting Started with E2B

Setting up E2B involves straightforward SDK integration. It’s as simple as installing a package and writing a few lines of code:

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

const sandbox = await Sandbox.create({ 
  apiKey: 'YOUR_API_KEY' 
})

// Execute code securely
const result = await sandbox.runPython('print("Hello, E2B!")')
console.log(result.output)

await sandbox.kill()

E2B manages the entire lifecycle automatically. You create a sandbox, execute code, and clean up. The platform handles provisioning, isolation, and resource management behind the scenes. It’s like having a personal assistant that takes care of all the infrastructure headaches so you can focus on building your AI features.

Getting Started with Daytona

Daytona’s integration is similarly straightforward, with support for both Python and TypeScript. The API feels familiar if you’ve worked with other cloud platforms:

import { Daytona } from '@daytonaio/sdk'

const daytona = new Daytona({ apiKey: 'YOUR_API_KEY' })

// Create sandbox
const sandbox = await daytona.create({ language: 'typescript' })

// Execute code
const response = await sandbox.process.codeRun('console.log("Hello, Daytona!")')
console.log(response.result)

// Clean up
await daytona.delete(sandbox)

Daytona provides additional capabilities beyond basic code execution. The SDK includes native Git operations, file system management, and LSP support, making it a more comprehensive platform for managing development environments as code. If your AI agents need to clone repos, manage files, or even get intelligent code completion, Daytona’s got you covered out of the box.

Comparative Advantages and Drawbacks

Now for the million-dollar question: Which platform should you choose? Like most things in tech, it depends on your specific needs. Let’s break down the pros and cons of each, so you can make an informed decision.

E2B Advantages

Enterprise adoption: With 88% of Fortune 100 companies using E2B, it’s battle-tested at scale. The platform has executed over 200 million sandboxes and handles millions of concurrent sessions.

Superior isolation: Firecracker’s microVM approach provides true hardware-level isolation, making it the best choice when security is paramount.

Performance at scale: E2B’s sophisticated architecture handles massive concurrency with predictable latency. Companies like Perplexity, Groq, and Hugging Face rely on E2B for their most demanding workloads.

Flexibility: E2B offers both managed SaaS and self-hosted options. Enterprise customers can deploy BYOC (Bring Your Own Cloud) to maintain complete control over their infrastructure.

If you’re building something that absolutely cannot fail and needs enterprise-grade security, E2B is your platform.

E2B Drawbacks

Session limitations: Free-tier sandboxes are limited to 1-hour sessions, though paid plans extend this to 5-10 minutes. Building persistent workloads requires workarounds or upgrading to Enterprise.

Cost at scale: While E2B offers a generous free tier, costs can add up with heavy usage. The pricing model includes both monthly subscription fees and per-vCPU-second charges.

Startup overhead: While 150ms is fast, it’s not instantaneous. Applications requiring sub-100ms response times may need optimization strategies.

E2B isn’t cheap, and if you’re on a tight budget or need ultra-low latency, these drawbacks might give you pause.

Daytona Advantages

Speed: Daytona’s 90ms environment creation is slightly faster than E2B’s 150ms, making it attractive for latency-sensitive applications.

Developer experience: The platform is designed for developers first. Features like built-in LSP support, Git integration, and file system operations reduce the friction of working with sandboxes.

Flexibility: The ability to configure isolation levels (default containers, Kata Containers, or Sysbox) lets teams choose their own security/performance trade-off based on use case.

Open-source options: Daytona’s commitment to open-source means you’re not locked into proprietary technology.

If you’re a developer who values speed and ease of use, Daytona’s approach will feel like a breath of fresh air.

Daytona Drawbacks

Isolation concerns: Default container-based isolation, while adequate for most AI agent scenarios, doesn’t provide the same security guarantees as microVMs. This matters when executing truly untrusted code.

Smaller ecosystem: Unlike E2B with its Fortune 100 adoption, Daytona has a smaller community and fewer battle-tested integrations with popular AI frameworks.

Production limitations: Daytona is primarily optimized for AI agent code execution. If you need to run databases, long-running services, or full applications beyond code snippets, you’ll need additional infrastructure.

Enterprise features: Daytona lacks some enterprise capabilities like BYOC and granular access controls that E2B provides.

For highly sensitive or enterprise-critical applications, Daytona’s container approach might not provide the peace of mind you need.

Real-World Use Cases

Let’s get practical. How do these platforms stack up in actual scenarios? Here are some common use cases and which platform might be the better fit.

When to Choose E2B

AI data analysis platforms: Companies like Perplexity use E2B to let users analyze datasets and create visualizations through natural language interfaces. The security guarantees and scale make it ideal.

Code generation tools: If you’re building a tool that generates and executes code (like GitHub Copilot alternative), E2B’s proven track record at scale matters.

Enterprise AI agents: Organizations need the BYOC option and enterprise SLAs that E2B provides for mission-critical AI workflows.

Compliance-heavy industries: Finance, healthcare, and government sectors where audit trails and isolation are non-negotiable favor E2B’s architecture.

If your application deals with sensitive data or needs to meet strict regulatory requirements, E2B’s enterprise focus makes it the obvious choice.

When to Choose Daytona

AI agent development: Building agents that need to run code snippets with minimal latency? Daytona’s 90ms environment creation gives you an edge.

Multi-language environments: If your agents need to switch between Python, JavaScript, TypeScript, and other languages dynamically, Daytona’s language-agnostic approach is simpler.

Developer-friendly workflows: Teams that value built-in Git integration, LSP support, and file system operations appreciate Daytona’s holistic approach.

Cost-conscious startups: If you’re bootstrapping and want predictable costs without per-vCPU-second charges, Daytona’s simpler pricing is appealing.

For rapid prototyping, developer productivity, or applications where speed trumps absolute security, Daytona shines.

Integration Considerations

While E2B and Daytona aren’t typically used together (they solve the same problem), some teams might evaluate both before committing. Here’s how to think about it:

For hybrid approaches: Some organizations run Daytona sandboxes for development-time code execution and E2B for production workloads. This isn’t ideal but reflects the reality that different tools excel at different things.

API compatibility: Both platforms provide similar SDKs, so switching between them requires code changes but isn’t architecturally complex. This flexibility means you’re not permanently locked in.

Feature parity: As both platforms evolve, feature gaps narrow. E2B’s roadmap includes developer experience improvements, while Daytona is adding enterprise features.

The good news? You can start with one and switch to the other if your needs change. Neither platform is trying to trap you in vendor lock-in.

Emerging Trends in 2025

The sandbox and code execution landscape is rapidly evolving. As AI becomes more sophisticated, so do the tools we use to contain it. Here are some trends I’m watching closely:

Agent-specific optimizations: Both platforms are increasingly tuning for AI agent workflows rather than generic code execution. Expect more native LLM integrations and prompt-friendly APIs.

Security standardization: As untrusted code execution becomes mainstream, industry standards for isolation and audit trails are forming. Both platforms are positioning themselves as standards-compliant.

Multi-cloud flexibility: Enterprise customers increasingly demand the ability to run across multiple cloud providers. E2B’s BYOC option and Daytona’s flexibility are competitive advantages here.

Reduced cold start: Both platforms continue pushing the boundaries of startup latency. Expect sub-50ms creation times to become standard within 12-18 months.

The competition between E2B and Daytona is driving innovation at a breakneck pace. What seems impossible today will be routine tomorrow.

Conclusion: Choosing Your Path Forward

E2B and Daytona represent different philosophies on secure code execution. E2B prioritizes enterprise scale, security, and proven battle-readiness. Daytona emphasizes developer experience, speed, and flexibility. Neither is universally “better”—the right choice depends on your specific requirements.

If you’re building enterprise AI applications where security and scale are non-negotiable, E2B’s track record and architecture make it the safer choice. If you’re exploring AI agent development and want something optimized for developer velocity, Daytona’s approach is compelling.

The good news? The competition between these platforms is pushing both toward improvement. Features that seemed cutting-edge six months ago are becoming baseline expectations. As a developer in 2025, you have genuinely excellent options for safely executing AI-generated code at scale.

The future belongs to applications that can harness AI’s creative power while maintaining ironclad security. E2B and Daytona are the infrastructure that makes that possible. Now go build something amazing!