Tabby is a self-hosted AI coding assistant written in Rust that gives development teams a privacy-first alternative to GitHub Copilot. The entire system is self-contained — no external database, no cloud dependency, no code leaving your network. A single Docker command spins up a server that handles code completion and chat using models like StarCoder, CodeLlama, CodeGemma, or Qwen, running on consumer-grade GPUs (an RTX 3060 is enough for a small team) or even CPU-only setups for lighter workloads. With over 32,000 GitHub stars, it has become the go-to choice for organizations that need AI coding assistance without sending proprietary code to third parties.
Beyond basic autocomplete, Tabby includes an Answer Engine that functions as an internal knowledge hub for engineering teams — developers ask questions directly in their IDE and get answers grounded in the team's own codebase, documentation, and Git history. Repository-level context indexing means suggestions account for project-wide patterns, naming conventions, and dependencies rather than generating code in a vacuum. The tool also supports GitLab merge request indexing, custom documentation ingestion via REST APIs, and inline chat for contextual collaboration. IDE extensions cover VS Code, JetBrains, Neovim, and Vim, with an OpenAPI interface for integrating into cloud IDEs or custom toolchains.
Enterprise features include LDAP authentication, GitHub and GitLab SSO, team usage analytics with per-developer reports, and audit logging. Tabby can be deployed via Docker, SkyPilot for multi-cloud GPU clusters, or BentoCloud for managed inference. Version 0.30 added GitLab merge request context, and an agent mode is in private preview for more autonomous coding tasks. For teams that want the productivity gains of AI coding tools but cannot accept the intellectual property and compliance risks of cloud-based solutions, Tabby fills a gap that few other tools address at this level of maturity.
