Tabby

Tabby is a self-hosted, open-source AI coding assistant built by TabbyML. It offers an on-premises alternative to GitHub Copilot, letting every team set up its own LLM-powered code completion server with ease — self-contained, with no need for a database management system or cloud service, and support for consumer-grade GPUs.

For teams with strict data-privacy requirements or air-gapped environments, Tabby brings AI code completion, an answer engine, and inline chat entirely under your control.

Key Features

1. Fully Self-Hosted

Run Tabby your way, on your terms. No external DBMS, no cloud service — everything runs in your infrastructure, keeping code private.

2. Code Completion

Intelligent, real-time AI code completion that understands your coding context. Tabby achieves accurate streaming and cancellation with adaptive caching for sub-second completions.

3. Answer Engine

Ask questions and get instant, reliable answers within your IDE — powered by a central knowledge engine that can integrate with your internal engineering data (added in v0.13.0).

4. Inline Chat

Collaborate with the AI assistant in real time without leaving your editor.

5. Consumer-Grade GPU Support

Optimized to run on affordable consumer hardware, with flexible inference options (CPU, CUDA, Metal) and support for popular coding models like CodeLlama, StarCoder, CodeGen, and Qwen.

6. Rich Integrations

OpenAPI interface, IDE extensions (VS Code, Vim, IntelliJ), repo-context via Tree-sitter tagging, GitLab SSO, self-hosted GitHub/GitLab, and an admin UI.

Use Cases

• Data-Privacy Teams: Get AI coding assistance without sending code to third-party clouds.
• Air-Gapped Environments: Run fully offline in isolated networks.
• Enterprises: Self-hosted completion with GitLab SSO and admin controls.
• Cost Control: Use open-weight models on your own hardware to avoid per-seat subscription fees.

Pricing

Tabby is free and open source. You pay only for the hardware you run it on:

• Community Plan: Local-first deployment, fully free.
• Team / Enterprise Plans: Enhanced security support and flexible deployment options for organizations.

Model inference costs are entirely under your control — run open-weight models locally or bring your own infrastructure.

Advantages & Unique Selling Points

1. Complete Data Control: Your code never leaves your infrastructure.
2. Zero External Dependencies: Self-contained, no DBMS or cloud required.
3. OpenAPI Interface: Easy to integrate with existing infrastructure, including cloud IDEs.
4. Cost Predictability: Consumer-grade GPU support means low-cost, predictable operation.

User Reviews

"Tabby is a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Copilot." — TabbyML GitHub README

"With Tabby, every team can set up its own LLM-powered code completion server with ease." — Tabby Documentation

"Self-contained, with no need for a DBMS or cloud service. Supports consumer-grade GPUs." — Tabby project description

Getting Started

1. Deploy: Run a single Docker command, e.g. docker run -it --gpus all -p 8080:8080 -v $HOME/.tabby:/data tabbyml/tabby serve --model StarCoder-1B --device cuda --chat-model Qwen2-1.5B-Instruct.
2. Install an Extension: Add the Tabby extension to VS Code, Vim, or IntelliJ.
3. Connect: Point the extension at your Tabby server URL.
4. Configure: Set models, inference options, and repo context from the admin UI.

Limitations

• Hardware Required: Self-hosting means you manage the GPUs, storage, and operations yourself.
• Setup Complexity: Deployment and model configuration require technical know-how.
• Model Quality: Completion quality depends on the open-weight models you choose.
• Smaller Ecosystem: Smaller community than GitHub Copilot or commercial assistants.

Conclusion

Tabby is the go-to open-source choice for teams that want AI code assistance without surrendering data control. Its self-contained architecture, consumer-GPU support, and answer engine make it a practical, private Copilot alternative for enterprises and privacy-conscious developers alike. If your team operates in air-gapped or regulated environments — or simply prefers owning its infrastructure — Tabby deserves strong consideration.

Recommended for: Privacy-sensitive teams, air-gapped environments, enterprises wanting self-hosted AI, and developers comfortable running their own models.

Not recommended for: Developers who want zero-setup, fully managed AI assistance without infrastructure responsibility.