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GitHub Copilot vs Tabby
GitHub Copilot vs Tabby
A side-by-side look at GitHub Copilot (the paid SaaS) and Tabby (the open source alternative). Use this page to decide if the switch fits your team and workflow.
| GitHub Copilot | Tabby | |
|---|---|---|
| Tagline | AI pair programmer trained on public code. | Self-hosted AI coding assistant you can run on your own GPU. |
| License | Proprietary SaaS | Apache-2.0 (community) / commercial (enterprise) |
| Pricing | Individual $10/month; Business $19/user/month. | Free to self-host |
| Self-host option | No | Yes — difficulty 3/5 |
| Hosted cloud available | Yes (only option) | No |
| Desktop apps | Varies by product | Web only |
| Mobile apps | Official apps typically available | None official |
Ad slot — between tables
Best for
Teams that need fully self-hosted completions on their own GPU.
Tabby strengths
- Fully self-hosted code completion.
- VS Code, IntelliJ, vim extensions.
- Runs on consumer GPUs with quantized models.
Tabby weaknesses
- GPU strongly recommended for good latency.
- Larger install footprint.
- Enterprise features gated.
What's the catch with GitHub Copilot?
- Code sent to Microsoft/OpenAI for completions.
- Subscription-only.
- Licensing questions around training data still unresolved for some teams.
Still unsure?
Check the full list of alternatives to GitHub Copilot: see GitHub Copilot alternatives, or learn more about Tabby on its project page.