Open Source Daytona Alternatives
A curated collection of the 2 best open source alternatives to Daytona.
The best open source alternative to Daytona is Coder. If that doesn't suit you, we've compiled a ranked list of other open source Daytona alternatives to help you find a suitable replacement. Other interesting open source alternative to Daytona is E2B.
Daytona alternatives are mainly Version Control & Collaboration but may also be AI Development Platforms. Browse these if you want a narrower list of alternatives or looking for a specific functionality of Daytona.
Self-hosted development environments defined in Terraform, for platform teams that want cloud workspaces and coding agents on their own infrastructure.
Every developer laptop drifts away from production eventually. Coder answers that with self-hosted cloud development environments and AI coding agents: workspaces are declared once and provisioned on infrastructure the company controls, which turns onboarding from days into seconds.
Workspaces are defined in Terraform, so one can be an EC2 virtual machine, a Kubernetes pod or a Docker container while the templates stay the same for everyone. Connections travel through a secure Wireguard tunnel, and idle resources shut down automatically to hold the bill down.
Two halves of the platform matter to different readers.
- Templates: Terraform files describe the infrastructure behind every workspace, with a public registry of ready-made ones.
- Editor access: extensions for VS Code and JetBrains Toolbox open a workspace in one click, and dev containers build from a devcontainer.json.
- Coder Agents: a native coding agent whose loop executes in the control plane, running any model from Anthropic, OpenAI, Google, Bedrock or a self-hosted endpoint.
- Credential isolation: no model credentials sit inside workspaces, and every agent action carries the identity of the user behind it.
- AI Gateway: centralised authentication, auditing and cost tracking for AI tooling across the deployment.
An install script covers Linux and macOS, with binaries and installers for Windows, and running the server is enough to try it locally. Production deployments add PostgreSQL 13 or later and an external access URL, with validated architectures published for sizing.
Cloud sandboxes for running AI-generated code safely, aimed at developers building agents that need to execute what a model writes.
Running code that a language model just wrote is the point where an agent stops being a demo and becomes a liability. E2B is open source infrastructure for exactly that problem, providing secure isolated sandboxes in the cloud where generated code can execute without touching your own machines.
Sandboxes are started and controlled from a JavaScript SDK or a Python SDK. Create one, run shell commands inside it, read the output back into your program, and dispose of it when the task is finished. An API key from the dashboard is the only setup beyond installing the package.
Three packages cover the common shapes of agent work.
- Core SDK: create sandboxes and run commands, with stdout returned to the caller.
- Code Interpreter: execute code directly through runCode and run_code for interpreter style workloads.
- Desktop: mouse, keyboard, screenshot, application and desktop streaming APIs for computer use agents.
A cookbook of examples shows the SDKs working with different models and AI frameworks, and the documentation covers the rest of the surface area.
If you would rather not depend on the hosted service, the infrastructure is published separately and deploys with Terraform, guided by a self-hosting document. AWS and Google Cloud are supported today, while Azure and a general Linux machine remain unchecked items on the project's own list.
