Daytona vs E2B: Which Is Better in 2026?

Daytona is cloud infrastructure for safely running AI-generated and untrusted code in isolated sandboxes. It gives AI agents and applications a secure runtime to execute code, spinning up isolated environments in milliseconds — the "more than a sandbox" layer purpose-built for agentic workloads rather than generic code isolation.

What is Daytona?

Daytona provisions isolated sandbox environments on demand, where AI-written or otherwise untrusted code can run without endangering your systems. It streams process output in real time, supports full file-system operations, native Git and multiple languages, and — crucially — creates sandboxes in under 90 milliseconds, so agents can parallelize massively without waiting on slow environment setup.

Who it's for

Daytona is built for AI agent developers and companies building agentic workflows, teams running code evaluations or interpreters, and anyone needing secure, isolated execution at scale. Customers like SambaNova, Writer.com and Mastra AI use it for exactly this — running model-generated code safely and fast.

Key features

• Sub-90ms sandbox creation and startup
• Real-time process execution and output streaming
• Full file-system CRUD with permission controls
• Native Git integration with secure credential handling
• Environment snapshots that save, restore and resume
• Indefinitely-running sandboxes and shared volumes for stateful workflows
• Virtual desktops (Linux, macOS, Windows) with programmatic control
• SDKs for Python, TypeScript, Ruby, Go and Java, plus a REST API

Speed built for parallel AI work

Daytona's headline strength is provisioning speed. Sub-90ms sandbox creation means an agent can spin up hundreds or thousands of isolated environments almost instantly — essential when you're evaluating many code candidates or scaling an agentic pipeline. As a SambaNova executive put it, Daytona does sandbox provisioning "incredibly well," and that millisecond-level performance is what makes massive parallelization practical.

Stateful, long-running environments

Unlike throwaway sandboxes, Daytona supports persistence: you can snapshot an environment and restore or resume it later, keep sandboxes running indefinitely for long tasks, and share volumes across isolated sandboxes. That stateful design fits real agent workflows, where an agent needs to pick up where it left off or maintain context across many steps rather than starting fresh every time.

Computer use, not just code

Daytona goes beyond executing snippets. It provides full virtual desktop environments — Linux, macOS and Windows — with programmatic control, SSH access, a web terminal and VS Code Browser integration. That "computer use" capability lets agents operate a real machine, not just a code runner, opening up workflows like automated testing, browsing and multi-app tasks inside a secure boundary.

Secure and cost-transparent

Security is central: isolated, customer-managed compute in your own cloud, secret management that keeps credentials outside sandboxes, and HIPAA, SOC 2 and GDPR compliance. Pricing is pay-as-you-go and granular — per-vCPU, per-GiB memory and storage by the hour, with GPU options and a $200 free credit — so you pay for exactly the compute your agents use, and can scale from experiments to production predictably.

Fits your language and tooling

Daytona meets developers in their existing stack. It supports Python, TypeScript, Ruby, Go and Java via pip and SDKs, works with the Docker ecosystem (images, Dockerfiles and Compose) plus a declarative image builder, and offers regional deployment across US, EU and Asia. Language Server Protocol support even brings real code analysis inside the sandbox — so agents get a proper development environment, not a bare shell, wherever your users are.

Why choose Daytona

For teams building AI agents or running untrusted code, Daytona is a purpose-built, secure runtime that's fast enough to scale. Its millisecond sandboxes, stateful environments, full computer-use desktops, strong isolation and transparent pricing make it a practical foundation for agentic products that need to execute code safely — without building and maintaining that infrastructure yourself.

The moment you let an AI agent actually run code, you have a serious problem: you can't just execute arbitrary AI-generated code on your own servers and hope for the best. E2B solves exactly that — it provides open-source, isolated cloud sandboxes where AI agents can securely execute code and use real-world tools, safely walled off from everything else.

A safe place for agents to run code

E2B gives AI agents isolated sandbox environments to run Python, JavaScript and other languages inside microVM-based containers powered by Firecracker technology (the same isolation tech behind serverless platforms). That means an agent can write and execute code, install packages, read and write files, and use a terminal — all without any risk to your infrastructure. For anyone building agentic AI, that secure execution layer is a foundational, hard-to-build-yourself piece.

Fast and long-running

Performance is a big deal for agent workflows, and E2B delivers sub-200ms startup times so spinning up a sandbox doesn't bottleneck your agent, plus support for sessions up to 24 hours for longer, stateful tasks. It integrates with major LLM providers including OpenAI, Anthropic, Mistral and Meta's models, so it drops into whatever AI stack you're using. It pairs naturally with vector databases like Weaviate and Qdrant in the broader agent infrastructure toolkit.

Built for real agent use cases

E2B is designed for the workloads people are actually building: deep-research agents, data-analysis tools, coding assistants and reinforcement-learning systems. Anywhere an AI needs to genuinely do things — run a calculation, execute a script, manipulate files — rather than just talk, E2B is the safe environment where that happens.

Open and enterprise-ready

Being open source means transparency and the option to inspect or self-host, while the platform is built to enterprise-grade standards for companies deploying agents in production. That combination is increasingly what serious AI teams look for.

Who it's for

E2B suits enterprise companies, AI startups and developers building agentic workflows — anyone whose AI needs to execute code and use tools securely rather than merely generate text.

Pricing

E2B is freemium with paid tiers (including a Pro plan) for heavier usage; you can start free to build and test your agents' code execution before scaling up. Being open source, self-hosting is also on the table for teams that want full control.

Bottom line: E2B is the secure, open-source sandbox layer for AI agents — fast, isolated microVMs where agents can safely run code and use real tools, making it foundational infrastructure for anyone building serious agentic applications.