Daytona
What is Daytona?
Daytona is an isolated sandbox platform for AI platform teams that need safe execution for generated code and agent workflows. It combines Fast, Scalable, Stateful Infrastructure, Separated & Isolated Runtime Protection, Massive Parallelization, and Environment Snapshots, with SDK, RESTful API, and CLI control. Customers include LangChain, SambaNova, and Stanford's CS Department. Pricing is usage-based, with vCPU at $0.0504/h, GPU Nvidia H100 at $3.95/h, and Storage at $0.000108/h.
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At a glance
- Daytona is best for AI platform teams who need isolated, fast sandboxes for agent code execution.
- Yes — The page advertises a RESTful API and programmatic control for creating sandboxes, executing code, and managing files and Git operations.
What does Daytona do?
Daytona spins up isolated sandboxes for AI-generated code and agent workflows, then lets teams drive them through SDKs, a RESTful API, and a CLI. Its runtime combines process execution, file system operations, Git integration, and runtime configuration so agents can create, edit, run, and tear down environments without touching the host machine. The platform is built around stateful environment snapshots and unlimited persistence, so work can continue across sessions instead of starting from scratch each time. At scale, Daytona creates sandboxes in under 90ms and supports concurrent workflows through massive parallelization. The docs describe full composable computers with a dedicated kernel, filesystem, network stack, and allocated vCPU, RAM, and disk, built on OCI/Docker compatibility. Customers including LangChain, Prosus, SambaNova, and Stanford's CS Department use it for sandboxed agent development, benchmarking, and regulated deployments. Daytona also offers self-hosted deployment for teams that want customer-managed compute and tighter control over infrastructure.
Why use Daytona?
- Sandboxes start in under 90ms, so agent loops can move from code to execution without long waits.
- Complete isolation with a dedicated kernel, filesystem, and network stack reduces risk to host infrastructure.
- Stateful snapshots and unlimited persistence let agents resume work instead of rebuilding environments every run.
- Massive parallelization supports concurrent workflows when many sandboxes need to run at once.
- Self-hosted and customer-managed compute options give teams more control over where workloads run.
Who is Daytona for?
- AI platform engineers who need safe execution environments for generated code.
- Developer tooling teams who want programmatic sandbox lifecycle control.
- ML and agent teams who need persistent environments across sessions.
- Infrastructure teams who need isolated compute with host protection.
- Organizations with regulated deployments who want customer-managed compute.
What are Daytona's key features?
Fast, Scalable, Stateful Infrastructure
Create sandboxes in under 90ms and keep state across runs with allocated vCPU, RAM, and disk, so agents can iterate without rebuilding environments.
Separated & Isolated Runtime Protection
Run code in complete isolation with a dedicated kernel, filesystem, and network stack, reducing blast radius when executing untrusted AI-generated code.
Massive Parallelization
Spin up many sandboxes at once for agent workflows and simulations, helping teams run parallel jobs instead of queueing work on one machine.
Process Execution
Execute commands and code through the RESTful API, giving teams programmatic control over sandbox processes for automation and agent-driven tasks.
File System Operations
Manage files inside sandboxes through the API, which lets agents edit, read, and organize project state without leaving the runtime.
Git Integration
Work with Git inside isolated environments, so code changes can be committed and synced as part of automated development flows.
Builtin LSP Support
Use built-in language server support for code intelligence inside the sandbox, improving editing and analysis for agent-generated projects.
Environment Snapshots
Save and restore stateful environment snapshots with unlimited persistence, making it easier to reproduce agent runs and resume work later.
What does Daytona integrate with?
- Git
- Slack
- GitHub
- Claude
- OpenCode
- Codex
- LangChain
What are Daytona's use cases?
Agent code execution
AI platform engineers use Daytona to run generated code in safe, isolated sandboxes, using Separated & Isolated Runtime Protection to keep host systems protected. They pair it with Process Execution and File System Operations to test agent output, inspect artifacts, and catch failures before anything reaches production.
Persistent environments for agents
ML and agent teams use Daytona to keep workspaces alive across sessions, using Stateful by Design and Environment Snapshots to resume experiments without rebuilding context. That persistence helps them continue long-running workflows, compare changes, and avoid losing intermediate state between runs.
Programmatic sandbox control
Developer tooling teams use Daytona to create and manage sandboxes from code, using Programmatic Control and Git Integration to automate environment setup, branch-based testing, and cleanup. They can also use Builtin LSP Support to make those environments feel ready for real development work.
Customer-managed regulated compute
Organizations with regulated deployments use Daytona to keep execution inside customer-controlled infrastructure, using Customer-Managed Compute and Enterprise Compliance to satisfy internal security requirements. Infrastructure teams still get Fast, Scalable, Stateful Infrastructure for isolated workloads without giving up control.
How does Daytona work?
- Connect your first Git repository or integration, then spin up an Instant Sandboxes environment from the dashboard or API so code has a clean place to run.
- Choose the runtime size and isolation level, using Fast, Scalable, Stateful Infrastructure and Separated & Isolated Runtime Protection to keep workloads responsive and host systems protected.
- Run code, inspect files, and manage branches with Process Execution, File System Operations, and Git Integration while Builtin LSP Support helps the environment behave like a real dev setup.
- Save progress with Environment Snapshots or Volumes, then restore the same state later so agents and engineers can continue work without rebuilding context.
- Scale out parallel jobs with Massive Parallelization, or connect through SSH Access and the Web Terminal when you need direct debugging and ongoing operational control.
How much does Daytona cost?
Storage: GiB
$0.000108/h- Price per GiB after first 5 free
Frequently asked questions
What is Daytona?
Daytona is an isolated sandbox platform for AI platform teams that need safe execution for generated code and agent workflows. It combines Fast, Scalable, Stateful Infrastructure, Separated & Isolated Runtime Protection, Massive Parallelization, and Environment Snapshots, with SDK, RESTful API, and CLI control. Customers include LangChain, SambaNova, and Stanford's CS Department. Pricing is usage-based, with vCPU at $0.0504/h, GPU Nvidia H100 at $3.95/h, and Storage at $0.000108/h.
How much does Daytona cost? Is it free?
Daytona has 5 paid plans: Compute: vCPU at $0.0504/h, Compute: GPU Nvidia H100 at $3.95/h, Compute: GPU Nvidia RTX PRO 6000 at $3.03/h.
What is Daytona used for? Who is it for?
Daytona is used for Fast, Scalable, Stateful Infrastructure, Separated & Isolated Runtime Protection, and Massive Parallelization. It's built for AI platform engineers, Developer tooling teams, and ML and agent teams.
Does Daytona have an API and what does it integrate with?
The page advertises a RESTful API and programmatic control for creating sandboxes, executing code, and managing files and Git operations. It integrates with Git, Slack, Google, GitHub, Claude, and 3 more.
Editor's read
Check whether your workload needs self-hosted deployment or customer-managed compute before committing. Daytona also prices by usage, so verify expected vCPU, GPU, memory, and storage consumption against the hourly rates.
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