Self-Hosted Alternatives to GitHub Copilot
Why Replace GitHub Copilot?
Cost: GitHub Copilot costs $10-19/month per developer ($120-228/year). For a team of 5 developers, that’s $600-1,140/year. Self-hosted code completion is free after the hardware investment.
Privacy: Copilot sends your code to GitHub/Microsoft servers for processing. If you work with proprietary code, sensitive algorithms, or client data, self-hosted alternatives keep everything on your infrastructure.
Control: Microsoft can change Copilot’s model, pricing, or policies at any time. They’ve already changed what’s included in different tiers. Self-hosted solutions are yours to control.
Compliance: Some organizations (government, healthcare, finance) prohibit sending code to third-party cloud services. Self-hosted code AI satisfies these compliance requirements.
Best Alternatives
Tabby — Best Dedicated Server
Tabby is a self-hosted code completion server with an admin dashboard, user management, and repository indexing. It indexes your codebase for context-aware suggestions — the closest experience to Copilot’s repo-aware completions.
IDE support: VS Code, JetBrains, Vim/Neovim.
Requires: NVIDIA GPU with 4+ GB VRAM (or CPU mode, slower).
Read our Tabby guide | Tabby vs Continue
Continue.dev + Ollama — Best Flexible Setup
Continue.dev is an open-source VS Code / JetBrains extension that connects to any LLM backend. Pair it with Ollama for a completely self-hosted setup. You get chat, autocomplete, and inline editing powered by any model Ollama supports.
Advantages over Tabby: Use different models for different tasks (fast small model for autocomplete, large model for chat). No dedicated server needed — just Ollama running locally or on a server.
IDE support: VS Code, JetBrains.
vLLM — Best for Team Serving
vLLM serves code models to multiple developers simultaneously with high throughput. Pair with Continue.dev extensions for a team-scale setup.
Best for: Teams of 5+ developers who need fast, concurrent code completions.
Migration Guide
From Copilot to Tabby
- Deploy Tabby on a machine with an NVIDIA GPU
- Add your repositories in Tabby’s admin dashboard for context indexing
- Install the Tabby extension in VS Code or JetBrains
- Point the extension at your Tabby server URL
- Disable GitHub Copilot extension to avoid conflicts
From Copilot to Continue + Ollama
- Install Ollama on your development machine or a server
- Pull a code model:
ollama pull deepseek-coder-v2:16b - Pull a fast model for autocomplete:
ollama pull starcoder2:3b - Install Continue.dev extension in VS Code
- Configure Continue to use your Ollama instance
- Disable GitHub Copilot extension
Cost Comparison
| Copilot Individual | Copilot Business | Self-Hosted (Tabby) | Self-Hosted (Continue + Ollama) | |
|---|---|---|---|---|
| Monthly (per dev) | $10/month | $19/month | $0 | $0 |
| Annual (5 devs) | $600/year | $1,140/year | ~$120/year (electricity) | ~$60/year (electricity) |
| 3-year (5 devs) | $1,800 | $3,420 | $360 + hardware | $180 + hardware |
| GPU cost | $0 | $0 | $300-800 (once) | $0-800 (optional) |
| Code privacy | No | Partial | Complete | Complete |
| Internet required | Yes | Yes | No | No |
What You Give Up
- Model quality: Copilot uses GPT-4-class models fine-tuned specifically for code. Open-source code models (StarCoder, DeepSeek Coder) are good but not quite at the same level for complex multi-file completions.
- Speed: Copilot runs on Microsoft’s infrastructure — completions are fast regardless of your hardware. Self-hosted speed depends on your GPU.
- Multi-file context: Copilot can reference your entire workspace through GitHub’s infrastructure. Self-hosted alternatives have improving but more limited context windows.
- GitHub integration: Copilot integrates with GitHub PRs, issues, and repos. Self-hosted alternatives don’t have this integration.
- Chat: Copilot Chat is tightly integrated with the IDE. Continue.dev provides similar functionality, Tabby is more focused on completions.
- Zero maintenance: Copilot just works. Self-hosted requires managing a model server, updates, and hardware.
For most code completion tasks — inline completions, function generation, docstrings — self-hosted alternatives work well. Complex multi-file refactoring is where Copilot still has an edge.
Frequently Asked Questions
Can self-hosted code completion work offline?
Yes. Both Tabby and Ollama run entirely on your machine with no internet connection required. This is a major advantage over Copilot, which requires constant connectivity. Airplane, air-gapped networks, and classified environments all work with self-hosted code AI.
What code models work best for self-hosted completion?
DeepSeek Coder V2 (16B) is the best overall open-source code model as of 2026. StarCoder2 (3B and 7B) is faster for inline completions. CodeLlama (13B) is solid for Python. For Tabby, the recommended models are StarCoder2-3B (fast autocomplete) and DeepSeek-Coder-V2-Lite (quality completions). Use smaller models for autocomplete speed and larger models for chat/refactoring.
How much GPU VRAM do I need for code completion?
StarCoder2-3B runs on 4 GB VRAM (fast autocomplete). DeepSeek-Coder-V2-16B needs 12+ GB VRAM (quality completions). A used RTX 3060 12GB (~$200) handles both use cases. For CPU-only mode, expect 2-5x slower responses — usable for chat, too slow for real-time autocomplete.
Is the completion quality as good as GitHub Copilot?
For single-file completions (function bodies, boilerplate, patterns), open-source models are 80-90% as good as Copilot. For multi-file context (understanding your entire codebase), Copilot has a significant edge. Tabby narrows this gap with repository indexing — it scans your codebase and uses it as context for suggestions.
Can I use self-hosted code AI with JetBrains IDEs?
Yes. Tabby has official plugins for IntelliJ IDEA, PyCharm, WebStorm, and all JetBrains IDEs. Continue.dev also supports JetBrains. Both provide inline completions and chat within the IDE — the same UX as Copilot’s JetBrains integration.
Can a team share one self-hosted code AI server?
Yes. Tabby has built-in user management and serves multiple developers from a single server. Deploy on a machine with a strong GPU (RTX 4090 or A100 for 5-10 concurrent users), and each developer’s IDE connects to the same Tabby endpoint. vLLM is designed specifically for high-throughput multi-user serving.
Is it worth switching from Copilot to self-hosted?
If your primary motivations are cost savings and code privacy, yes. A team of 5 developers saves $600-1,140/year by switching to Tabby. If you work with proprietary or sensitive code, the privacy benefit alone justifies the switch. If you primarily value Copilot’s multi-file context and GitHub integration, the self-hosted alternatives aren’t quite there yet.
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