Daytona vs Lightning AI
A side-by-side comparison of Daytona and Lightning AI, two Infra tools, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
At a glance
| Attribute | Daytona | Lightning AI |
|---|---|---|
| Category | Infra | Infra |
| Pricing | FREEMIUM | FREEMIUM |
| License (differs) | Open core | Proprietary |
| Deployment | Hybrid | Hybrid |
| Platforms (differs) | API, CLI | Web, CLI, API |
| Model support | Model-agnostic | Model-agnostic |
| Vendor (differs) | Daytona | Lightning AI |
| Capabilities (differs) |
|
|
The honest brief
Daytona
Boots an isolated sandbox with its own kernel and filesystem in under 90ms, so agents run untrusted code safely.
- Sub-100ms sandbox start
- Full isolated kernel + FS
- SDKs in many languages
- Open-source, self-host option
- AGPL-3.0 may deter some
- Infra to manage if self-hosted
- Newer entrant
Lightning AI
From the PyTorch Lightning team — Studios are persistent GPU workspaces you pause and resume, not throwaway notebooks.
- Pause/resume persistent GPU Studios
- Code, train, serve, build agents in one place
- Bring-your-own-cloud for enterprise
- Monthly free GPU credits
- Pay-as-you-go can add up
- Tied to its Studio environment
- Less raw control than bare cloud
When to pick which
Across the signals we compare, Daytona and Lightning AI differ on license and platforms:
- License
- Open core · Proprietary
- Platforms
- API, CLI · Web, CLI, API
Their capability lists differ in recorded depth — compare the full lists above before deciding.