Tabby
Self-hosted AI coding assistant with a REST API. Tabby runs locally or on-premises, providing GitHub Copilot-like code completions using open-source LLMs (CodeLlama, StarCoder, DeepSeek Coder). Ships with IDE plugins (VS Code, JetBrains, Vim/Neovim) and a REST API for custom integrations. Enterprise features include RBAC, Git context indexing, and a team management UI. Core value: full data privacy with no code sent to external services.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
Full data sovereignty — no code leaves the network. Self-hosted eliminates SaaS data retention risks. TLS configuration is admin responsibility. Apache 2.0 licensed, Rust-based server.
⚡ Reliability
Best When
You have security requirements preventing code from leaving your network and have GPU infrastructure available for inference. Best for privacy-first teams or enterprises with compliance requirements.
Avoid When
You can use cloud-based tools and prioritize completion quality over privacy — Cursor or GitHub Copilot will outperform self-hosted open models.
Use Cases
- • Provide AI code completions to development teams without sending proprietary code to external APIs — all inference runs on-premises
- • Build custom coding assistant workflows using Tabby's REST API for code completion and repository context retrieval
- • Index team repositories in Tabby's context engine to provide codebase-aware completions aware of internal patterns and libraries
- • Offer AI coding assistance in air-gapped environments where external API access is prohibited by security policy
- • Run as a shared team coding assistant on a GPU server, providing Copilot-equivalent features without per-seat SaaS costs
Not For
- • Teams without GPU infrastructure — Tabby requires a GPU for good performance; CPU-only inference is extremely slow
- • Users wanting the absolute best code completion quality — GitHub Copilot (GPT-4) and Cursor (Claude) have higher quality than available open-source models
- • Quick setup without infrastructure — requires Docker/GPU server setup vs. simple SaaS signup
Interface
Authentication
Bearer token auth via Authorization header. Tokens created in Tabby admin UI. Enterprise version adds LDAP/SSO integration and per-user token management.
Pricing
Core Tabby is Apache 2.0 open source. Enterprise adds LDAP, RBAC, and admin dashboard. Infrastructure costs (GPU server) are the primary cost driver.
Agent Metadata
Known Gotchas
- ⚠ Tabby requires a CUDA-capable GPU for production use — CPU inference is available but 10-50x slower, making interactive completions impractical
- ⚠ Model download on first startup can take 10-30 minutes depending on model size — health check endpoint (/health) returns unhealthy until model is loaded
- ⚠ Repository context indexing is asynchronous — after configuring a Git repository, wait for the indexer to complete before expecting context-aware completions
- ⚠ Tabby's completion API format is compatible with GitHub Copilot's API — IDE plugins using Copilot protocol work without modification
- ⚠ Tabby Enterprise LDAP integration requires network access to the LDAP server from the Tabby container — network policy must be configured correctly
- ⚠ Model selection affects VRAM requirements significantly — CodeLlama 7B requires ~8GB VRAM, 34B requires ~70GB; choose model based on available hardware
Alternatives
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Scores are editorial opinions as of 2026-03-07.