Tabby - Self-Hosted GitHub Copilot Alternative

Tabby is basically GitHub Copilot that runs on your own hardware instead of sending your code to Microsoft. That's it. That's the entire value proposition.

It's open source, so you can see exactly what it's doing with your code (spoiler: keeping it local). Works with the same models as other coding assistants - CodeLlama, StarCoder, whatever. The difference is your company's trade secrets don't get uploaded to someone else's servers.

Has something like 32k GitHub stars last I checked, which is decent for a self-hosted tool. Not massive, but there's an active community that actually fixes bugs instead of just requesting features.

How It Actually Works

Everything runs locally. You spin up a Docker container or install it directly, feed it a model (they support StarCoder, CodeLlama, DeepSeekCoder, and Qwen2.5-Coder), and it starts serving completions. No external database bullshit, no cloud dependencies.

The IDE extensions work in VS Code, JetBrains stuff, Neovim, and Eclipse. VS Code extension is the most polished - the others work but you can tell they're not the priority. You get real-time completions and a chat interface that actually understands your codebase context.

Has an API if you want to build custom integrations. Runs on consumer GPUs fine - you don't need some enterprise datacenter setup.

Why People Actually Use This

Your code stays local: Microsoft can't see it, train on it, or accidentally leak it. If your company has lawyers who freak out about IP leaving the building, this solves that problem.

Costs nothing except hardware: No per-seat licensing bullshit. You pay for whatever GPU you run it on, that's it. For teams over 10-20 people, this gets way cheaper than Copilot subscriptions.

You can actually see what's happening: Open source means when it breaks (and it will), you can debug it. When GitHub Copilot starts suggesting your internal API keys, you're stuck filing support tickets. With Tabby, you can fix it.

The main downside is setup complexity. GitHub Copilot is one button. Tabby requires you to understand Docker, GPU drivers, and model management. Expect to burn a weekend getting everything working. But if your legal team won't let you use cloud AI tools, it beats coding without any assistance.

Alternative self-hosted solutions like Continue.dev and Codeium exist, but Tabby has better documentation, more active development, and stronger community support. For enterprise teams, this means fewer operational surprises and faster resolution when things inevitably break.

The Reality of Setup

Setup is a pain in the ass if you don't have the exact hardware they tested with. Here's what actually works:

If you have an NVIDIA GPU:

## First, make sure your NVIDIA drivers don't suck
nvidia-smi

## This is the official command (use -d to run in background):
docker run -it --gpus all -p 8080:8080 -v $HOME/.tabby:/data \
  registry.tabbyml.com/tabbyml/tabby serve --model StarCoder-1B --device cuda

If that fails with docker: Error response from daemon: could not select device driver, your Docker doesn't have GPU support. Good luck with that rabbit hole.

If you don't have NVIDIA:

## CPU-only mode (prepare for disappointment)
docker run -it -p 8080:8080 -v $HOME/.tabby:/data \
  registry.tabbyml.com/tabbyml/tabby serve --model StarCoder-1B --device cpu

This will work but be slower than typing by hand. Apple Silicon Macs do okay with CPU mode, x86 is brutal.

Hardware Reality Check

1B models (StarCoder-1B):

• VRAM needed: 2-4GB minimum, 8GB to not hate life
• Quality: Better than nothing, worse than Copilot
• Speed: Usable on RTX 3060+, painful below that

7B models (CodeLlama-7B):

• VRAM needed: 14GB minimum despite what they claim
• Quality: Actually decent, comparable to early Copilot
• Speed: RTX 4070+ or you'll be waiting

13B+ models:

• VRAM needed: 24GB+ (RTX 4090, A100, etc.)
• Quality: Approaches current cloud tools
• Speed: If you have to ask, you can't afford it

The Shit That Will Break

Windows: Docker Desktop's WSL2 integration randomly breaks. When it does, you'll spend 2 hours reinstalling everything.

CUDA Version Mismatches: The container expects CUDA 11.x but your drivers are 12.x. Or vice versa. There's always a mismatch.

Memory Issues: The model says it needs 8GB VRAM but actually needs 12GB because of overhead. Crashes with cryptic CUDA out-of-memory errors.

Port Conflicts: Something else is using port 8080. Change it to 8081 or whatever.

IDE Setup (The Easy Part)

1. Install the VS Code extension
2. Point it to http://localhost:8080
3. Create an account through the web UI
4. It works, assuming your Docker container hasn't crashed

The JetBrains plugin works but feels like an afterthought. Neovim setup requires lua configuration that assumes you know what you're doing.

When It Actually Works

The chat feature is surprisingly good at understanding your codebase. You can ask it about specific functions and it gives relevant answers instead of generic Stack Overflow responses.

Code completions are solid if you're using a 7B+ model. The 1B models complete obvious stuff but miss the clever suggestions that make AI assistance worth it.

Indexing your codebase takes forever the first time (couple hours for large repos) but then it actually knows your internal APIs and patterns.

Don't expect the "55% faster coding" bullshit from some research paper. You'll get maybe 10-20% if you're lucky, but without sending your code to Microsoft.

Production Considerations

For production deployments, you'll need proper monitoring, backup strategies, and security hardening. The Tabby admin interface handles basic user management, but enterprise authentication requires LDAP integration. Consider load balancing for teams over 20 developers, and budget for GPU costs that can run $500-2000/month depending on your model choice. The deployment documentation covers basics, but expect significant customization for enterprise environments.