Daytona
Sub-100ms sandboxes for AI agents that write and run code
Overview
Daytona spins up isolated, stateful Docker sandboxes in under 90ms so AI agents can execute generated code safely. Open-source core (Apache 2.0) with a usage-based cloud tier, SDKs in five languages, and self-hosted/BYOC options for regulated teams.
Pricing
Pricing shown for reference only. These figures reflect RECATOOLS research as of 11 Jul 2026 and may be out of date or incomplete. This is not financial or purchasing advice — always confirm the current price on the provider’s official website before making any decision.
- 5GB free storage
- Instant sandbox creation
- Metered vCPU/memory/storage
- GPU sandbox options
- Volume discounts at scale
- Up to $50,000 in platform credits
- $10,000 upfront cash
- SSO and audit logs
- Bring-your-own-cloud (BYOC)
- Dedicated support
Use cases
What you can produce with Daytona
- Sub-90ms sandbox boot time
- Python, TypeScript, Ruby, Go and Java SDKs
- Persistent/stateful sandboxes across agent runs
- REST API for programmatic sandbox control
- Open-source core (Apache 2.0) on GitHub
- GPU-backed sandbox options
- Self-hosted / BYOC and Enterprise deployment
ASEAN Perspective
Daytona in Southeast Asia
ASEAN-region availability and pricing notes coming soon. Drop the editorial team a note via /contact/ if you can supply local context (Singapore/Malaysia/Indonesia/Thailand/Vietnam).
Daytona pivoted from a Docker-based dev-environment tool into infrastructure for running AI-generated code, and the pivot worked: boot times under 90ms, persistent state between agent runs, and SDKs for Python, TypeScript, Ruby, Go and Java. Teams that switched off raw Firecracker setups say keeping installed packages warm between runs cut their cold-start bill noticeably.
It's plumbing, not a product end users touch directly — evaluate it if you're building an agent platform that needs to execute untrusted code safely and fast, alongside E2B and Modal. The open-source core (72,000+ GitHub stars) is real, but usage-based billing means costs can creep if you don't watch concurrency. A Series A and $1M ARR within three months of the pivot suggest the market agrees this category matters.
What people say
Daytona started as an open-source alternative to Gitpod and Coder — a way to spin up consistent, cloud-hosted dev environments from a devcontainer.json. By 2026 the company had repositioned entirely around a different customer: AI agents that need somewhere safe to execute code they just wrote.
The technical story checks out. Sandboxes boot in under 90 milliseconds, several times faster than the Firecracker microVMs most agent frameworks used before. State persists between runs — installed pip or npm packages don't need reinstalling on every agent turn — which several teams cited on Hacker News and in r/MachineLearning threads as the reason they migrated off DIY sandbox setups. Northflank's and Better Stack's 2026 sandbox comparisons put Daytona in the top three for agent workloads, alongside E2B and Modal.
Funding backs up the traction: $31M raised total, including a $24M Series A in February 2026 led by FirstMark Capital, and the company says it crossed $1M ARR within three months of shipping the agent-runtime pivot. The GitHub repo (daytonaio/daytona) has passed 72,000 stars and ships under Apache 2.0.
Pricing is usage-based — per-second billing on vCPU, memory and storage, with $200 in free compute to start. That model rewards agent builders running lots of short-lived sandboxes but requires watching concurrency; nothing here is a flat monthly seat price. A startup program offers up to $50,000 in credits plus $10,000 upfront cash, and Enterprise adds SSO, audit logs and bring-your-own-cloud deployment.
The caveats are structural rather than about execution quality: this is a developer-facing infrastructure primitive, not something a non-technical team touches directly, and the agent-sandbox category — Daytona vs. E2B vs. Modal vs. raw Firecracker — is still young enough that betting deeply on one SDK carries real switching-cost risk. For teams building agent platforms that need to run arbitrary AI-generated code without babysitting infrastructure, it's a credible, well-funded choice with genuine open-source roots.
Summary of public user & expert reviews, compiled by RECATOOLS.
About this listing
This entry was compiled from publicly available data including Daytona's official website, press releases, documentation, and reputable third-party publications. RECATOOLS is not affiliated with Daytona unless explicitly stated.
Third-party AI tools update their pricing, features, availability, and policies frequently. Information here may be outdated by the time you read this — we make reasonable efforts to keep listings current, but cannot guarantee absolute accuracy.
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