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Daytona

Sub-90ms agent sandboxes — isolated computers with snapshots, volumes, Git and LSP tools, on Linux, Windows, or Android; AGPL self-host or managed cloud.

freemiumplatform
Updated Jun 11, 2026
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Daytona pivoted from dev-environment manager to agent infrastructure and found its market: sandboxes that start in under 90ms — isolated computers with dedicated kernel, filesystem, and network, lifecycle primitives, shared volumes, and agent tools — on Linux, Windows, or Android, with GPUs available. AGPL-3.0 self-hostable; cloud is usage-billed with signup credits.

Daytona is the category's speed-and-breadth play, and one of 2026's cleaner pivot stories: the dev-environment manager rebuilt itself as infrastructure for agent code execution — "give every agent a computer" — and the market answered (a FirstMark-led $24M Series A in February 2026, with LangChain among the customers).

Highlights

  • Sub-90ms sandbox creation — fast enough that agents treat computers as disposable per-step resources, not provisioned assets.
  • Real isolation — dedicated kernel, filesystem, and network stack per sandbox, with configurable vCPU/RAM/disk and GPU options.
  • Lifecycle primitives — start, stop, pause, snapshot; stateful sandboxes persist across runs, and volumes share data between them.
  • Multi-OS — Linux by default, with Windows and Android sandboxes (priced per vCPU-hour) — the unusual capability for testing and automation beyond the Linux monoculture.
  • Agent-shaped tooling — process exec, filesystem ops, Git operations, and LSP support exposed through SDKs in Python, TypeScript, Ruby, and Go.
  • Three deployment modes — managed cloud, fully self-hosted AGPL stack, or hybrid control-plane over customer-managed compute.

In an AI-assisted workflow

pip install daytona      # or: npm install @daytona/sdk
# sandbox = daytona.create(); sandbox.process.exec("python analyze.py")

The fit: agent systems that churn through many short-lived executions (where 90ms vs seconds compounds), need Windows/Android targets, or have compliance reasons to self-host the whole stack.

NOTE

Two npm scopes exist from the transition — legacy @daytonaio/sdk and current @daytona/sdk (both published, README uses the new one). And per-second billing means idle sandboxes cost money until stopped: wire cleanup into the agent's lifecycle.

Good to know

AGPL-3.0 (copyleft applies to self-host modifications), releases in fast lockstep across PyPI/npm. The honest caveat repeated from our research: the ~72k GitHub stars substantially predate the pivot — judge adoption by the 2026 product, not the counter. Category trade-offs versus E2B, Modal, and Vercel Sandbox: Sandboxing AI-Generated Code.

Frequently asked questions

Is this the same Daytona that managed dev environments?
Same company, different product. Daytona refocused from Codespaces-style dev environments to AI agent runtimes starting late 2024, relaunched in 2025, and raised a $24M FirstMark-led Series A (February 2026) on the new identity — customers include LangChain and Writer. Anything written about Daytona before 2025 describes the old product, and the repo's high star count largely predates the pivot.
What makes Daytona different from E2B?
Three visible bets: startup speed (sub-90ms creation, the headline claim), multi-OS sandboxes (Windows and Android alongside Linux — rare in the category), and deployment flexibility (managed cloud, fully self-hosted AGPL stack via Docker Compose, or a hybrid where Daytona runs the control plane over your compute). E2B counters with the code-interpreter ergonomics, Apache licensing, and the desktop product.
How is Daytona priced?
Usage-based: per-hour rates for vCPU, memory, and storage (GPUs like H100s available hourly), with free compute credits on signup and startup-program credits beyond that. Self-hosting under AGPL-3.0 trades the meter for ops — with copyleft obligations if you modify and serve it.

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