Daytona

The instant you let an AI agent write and run code, you face a hard question: where does that untrusted code execute safely, and fast enough to be useful? Daytona answers it directly — 'secure and elastic infrastructure for running your AI-generated code,' providing isolated sandboxes that spin up almost instantly.

Safe, isolated execution

Daytona creates isolated sandbox environments that execute code generated by AI agents and LLMs with complete runtime separation, protecting your systems from untrusted code. That isolation is non-negotiable for agentic AI: you simply can't run arbitrary AI-written code on your own infrastructure, and Daytona provides the safe, walled-off place where it can run without risk. It's the same critical need served by tools like E2B, with Daytona emphasizing extreme speed and stateful, long-running workflows.

Fast enough to feel instant

Speed is Daytona's headline: it creates sandboxes in under 90 milliseconds, so spinning up an environment doesn't stall your agent. For agent workflows that may create and tear down many environments, that near-instant startup is the difference between a snappy system and a sluggish one. It supports process execution with real-time output streaming, so you can watch code run as it happens.

Stateful and capable

Unlike ephemeral sandboxes, Daytona's are stateful by design, supporting long-running tasks and persistent agent workflows through environment snapshots and volume sharing across isolated instances. It also offers file-system operations with granular permissions, native Git integration and language-server-protocol support — so agents can do genuinely complex, real-world development work, not just run a quick script.

Built for AI builders

Everything about Daytona is oriented toward the emerging world of AI agents that write and execute code — evals, automation, coding assistants — where secure, fast, stateful execution is foundational.

Who it's for

Daytona suits developers building AI agents, LLM-powered applications, evaluation frameworks and automation tools, plus enterprises needing secure code execution and AI research teams running evals and RL experiments.

Pricing

Daytona uses pay-as-you-go compute pricing based on vCPU hours, GPU types, memory and storage, and includes $200 in free compute credit to start. That usage-based model plus generous starting credit lets you build and test agent execution before spending meaningfully.

The stateful design is a subtle but important differentiator: many sandboxes are throwaway, forcing an agent to rebuild its context every time, whereas Daytona's snapshots and shared volumes let an agent pick up exactly where it left off. For long-running or multi-step agent workflows — the kind that increasingly define serious AI applications — that persistence is what makes complex, real-world tasks feasible rather than a series of disconnected one-shot executions.

Bottom line: Daytona is secure, elastic infrastructure for AI-generated code — sub-90ms isolated sandboxes that are stateful and capable enough for real agent workflows, making it foundational runtime for anyone building code-executing AI.