Coding assistant

Open sourceApache 2.0Updated June 2026

Tabby

Self-hosted AI coding assistant for code completion and team-controlled coding workflows.

Intermediate · Self-hosted server via Docker or binary with VS Code and JetBrains IDE extensions connecting to the local endpoint

Editorial review

Reviewed byOpenSourcesAI EditorialLast updatedJune 2026SourcesTabby GitHub

Tool categories, pricing, source status, deployment options, and product claims can change quickly. Verify the official source before production or commercial use.

About Tabby

Self-hosted AI coding assistant for code completion and team-controlled coding workflows.

Best for: Engineering teams that need self-hosted code completion infrastructure with organization-level control over model choice, data residency, and usage analytics — without sending proprietary code to external cloud services.

Deployment: Self-hosted server via Docker or binary with VS Code and JetBrains IDE extensions connecting to the local endpoint

Skill level: Intermediate

Tradeoffs

Infrastructure burden falls on the team — model download, GPU or CPU server provisioning, uptime, and updates are internal responsibilities. Completion latency depends heavily on hardware; CPU-only deployments can feel slow on large fill-in-the-middle models. Feature scope is narrower than hosted code assistants with richer context window and agentic capabilities.

Related guides and resources

Explore step-by-step setup guides, comparisons, and stack recipes for this tool category.

AI tools directoryGuides libraryModel directory

Best for

Engineering teams that need self-hosted code completion infrastructure with organization-level control over model choice, data residency, and usage analytics — without sending proprietary code to external cloud services.

Why use it

Tabby runs entirely within your own infrastructure, making it the right choice when data governance, code privacy, or compliance requirements prohibit sending source code to third-party AI services. It adds team-level usage dashboards, completion acceptance rate tracking, and model configuration that hosted assistants don't expose to administrators.

Key features

  • Fully self-hosted completion server — source code never leaves the internal network boundary
  • GGUF quantized model support via llama.cpp backend alongside Hugging Face model loading for completion fine-tunes
  • VS Code and JetBrains IDE extensions that connect to the self-hosted endpoint with minimal per-developer configuration
  • Admin dashboard for team usage analytics, completion acceptance rates, and model performance monitoring across developers

Tradeoffs

Infrastructure burden falls on the team — model download, GPU or CPU server provisioning, uptime, and updates are internal responsibilities. Completion latency depends heavily on hardware; CPU-only deployments can feel slow on large fill-in-the-middle models. Feature scope is narrower than hosted code assistants with richer context window and agentic capabilities.

Alternatives

  • Continue
  • Sourcegraph Cody
  • Cline
CategoryCoding assistantLicenseApache 2.0DeploymentSelf-hosted server via Docker or binary with VS Code and JetBrains IDE extensions connecting to the local endpointModeSelf-hosted
Tabby GitHub →

OpenSourcesAI ecosystem connections

Use these next-step links to move from this profile into related tools, comparisons, guides, stacks, and curated shortlists.

Alternative solutions

Continue →Sourcegraph Cody →Cline →

Guides, comparisons, and resources

Best Local LLM Setup for Windows 2026 →What Is a Local LLM? →

Directory paths

AI tools directory →AI model directory →AI stack recipes →