AI Tool
Daytona pricing, features, company info, and alternatives
A factual product page for Daytona as sandbox infrastructure for AI-generated code and agent workflows.
Last updated April 2026 · Pricing and features verified against official documentation
Pricing
Current public pricing tiers on file for Daytona, last verified Apr 25, 2026.
Free trial
$0
Public signup is available with no credit card required and includes $200 in free compute.
Instant Sandboxes
$0.00001400 / vCPU/second
The pricing page also lists memory at $0.00000450 per GiB-second and storage at $0.00000003 per GiB-second, with hourly equivalents shown.
Startup Program
$0
Eligible startups can apply for up to $50k in credits.
Enterprise
Custom
Sales-led plan for businesses that need on-premise or higher-touch deployment.
What You Can Do With It
The main capabilities that shape how people use Daytona today.
Creates isolated sandboxes that agents and developers can manage through the dashboard, CLI, SDKs, and REST API.
Uses snapshots built from Docker or OCI images so teams can standardize dependencies and run Docker or Kubernetes workloads inside sandboxes.
Supports shared volumes backed by an S3-compatible object store for persistent data across multiple sandboxes.
Provides computer-use primitives plus an MCP server for agent-driven workflows.
Best For
Who Daytona is most clearly built for.
Developers building AI agents that need programmable code execution in isolated environments.
Teams that want reusable sandbox images, shared volumes, and customer-managed compute options.
Organizations that need a runtime layer they can access from SDKs, the CLI, or a REST API rather than a finished end-user assistant.
Company
Leadership and company context for Daytona Platforms Inc..
CEO
Ivan Burazin
Founders
Ivan Burazin, Vedran Jukić, Goran Draganić
Headquarters
New York, NY, USA
Platforms
Where you can use Daytona today.
Web dashboard
CLI
REST API
Python SDK
TypeScript SDK
Ruby SDK
Go SDK
Java SDK
Integrations
Notable connected tools and ecosystem hooks for Daytona.
Claude
Cursor
Windsurf
Codex
LangChain
OpenCode
Privacy Notes
Publicly stated data-handling notes that matter when evaluating Daytona.
The DPA says Daytona may process client personal data only to perform the service and says it does not sell that data or use it for targeted advertising.
The DPA says client personal data will be returned or deleted at the client's request or when the agreement ends, subject to legal retention requirements.
Daytona documents customer-managed compute options for custom regions, and its homepage says sandboxes can run on isolated compute in a customer's cloud or on-premises environment.
Compliance
Public compliance or enterprise-governance signals we found for Daytona.
SOC 2
HIPAA
GDPR
Access
How to integrate or build around Daytona.
Public API
Yes
Docs
Available
Alternatives
Other tools worth considering alongside Daytona.
Sandbox infrastructure for AI agents and code execution.
Cloud browser infrastructure platform with search, fetch, session replay, and deployment tools.
Serverless AI infrastructure platform for inference, training, batch jobs, sandboxes, and notebooks.
Browser-based coding environment for building and deploying apps with AI help.
Product Snapshot
Daytona is a sandbox infrastructure platform for running AI-generated code and agent workflows in isolated environments. Its public product surface centers on sandboxes, snapshots, volumes, SDKs, a CLI, and a REST API.
What You Can Do With It
- Create and manage sandboxes from the web dashboard, CLI, REST API, or the Python, TypeScript, Ruby, Go, and Java SDKs.
- Build reusable snapshots from Docker or OCI-compatible images and use them to standardize dependencies across runs.
- Attach shared volumes for persistent data across multiple sandboxes.
- Add programmatic computer-use workflows and connect sandboxes to agent tools through Daytona’s MCP server.
Why It Stands Out
Daytona combines isolated code execution, reusable sandbox images, shared storage, and agent-oriented access methods in one runtime layer. The docs also expose customer-managed compute options for teams that want more control over where sandbox workloads run.
Tradeoffs To Know
- Pricing is usage-based, so actual costs depend on compute, memory, and storage consumption after the included free credits are exhausted.
- Some capabilities described in the docs are still gated. For example, computer-use support for Windows and macOS is listed as private alpha as of April 25, 2026.
- The product is infrastructure for developers and platform teams, not a finished assistant for nontechnical users.
Sources
- daytona.io/pricing
- daytona.io/about
- daytona.io/privacy-policy
- daytona.io/terms-of-service
- daytona.io/dotfiles/the-2023-journey-of-daytona-a-year-in-review
- daytona.io/docs/en
- daytona.io/docs/en/snapshots
- daytona.io/docs/en/volumes
- daytona.io/docs/en/computer-use
- daytona.io/docs/en/getting-started
- daytona.io/docs/en/regions
- daytona.io/dpa
- daytona.io/docs/en/runners
- daytona.io
- daytona.io/docs/en/security-exhibit
- daytona.io/docs/tools/api