Tabby vs Continue: Self-Hosted Code AI Compared

Quick Verdict

Continue is the better choice for most developers who want AI code assistance with self-hosted models. It’s a VS Code / JetBrains extension that connects to any LLM backend (Ollama, LM Studio, vLLM) — no server to deploy. Tabby is the better choice if you want a dedicated self-hosted code completion server with repository-level context, team management, and a centralized deployment.

Overview

Both bring AI code assistance to your IDE using local or self-hosted models. They take very different approaches.

Tabby — Self-hosted code completion server. 25k+ GitHub stars. Written in Rust. Runs as a Docker container that serves code completions and chat to IDE extensions. Indexes your repositories for context-aware suggestions. Includes admin dashboard and team management.

Continue — Open-source IDE extension. 25k+ GitHub stars. Written in TypeScript. Installs directly in VS Code or JetBrains IDEs. Connects to any LLM backend (Ollama, OpenAI, Anthropic, LM Studio, etc.) for chat, autocomplete, and code editing. No server component required.

Feature Comparison

Feature	Tabby	Continue
Architecture	Server + IDE extension	IDE extension only
Self-hosted server	Yes (required)	No (connects to any LLM)
VS Code support	Yes	Yes
JetBrains support	Yes	Yes
Vim/Neovim support	Yes	Yes
Code autocomplete	Yes	Yes
Code chat	Yes	Yes
Inline editing	Yes	Yes (Cmd+I)
Repository context	Yes (code indexing)	Via @codebase context
Custom models	Yes (built-in serving)	Via Ollama, LM Studio, etc.
Team management	Yes (admin dashboard)	No (per-developer config)
Usage analytics	Yes (built-in)	No
SSO/LDAP	Yes (enterprise)	No
Multiple LLM backends	Limited	Yes (10+ providers)
RAG over docs	Yes (integrated)	Yes (via @docs context)
Custom slash commands	No	Yes
Model Context Protocol	No	Yes
GPU required on server	Recommended	N/A (depends on backend)
Docker deployment	Yes (official image)	N/A (extension only)
Default port	8080	N/A
License	Custom (Tabby License)	Apache 2.0

Installation Complexity

Tabby runs as a Docker container:

services:
  tabby:
    image: tabbyml/tabby:v0.32.0
    container_name: tabby
    ports:
      - "8080:8080"
    volumes:
      - tabby_data:/data
    command: serve --model StarCoder-1B --device cuda
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]
    restart: unless-stopped

volumes:
  tabby_data:

Then install the Tabby IDE extension and point it at your server. Tabby downloads and serves the model itself — no separate Ollama or LLM backend needed.

Continue requires no server. Install the VS Code or JetBrains extension:

1. Install the Continue extension from the marketplace
2. Configure ~/.continue/config.json to point at your LLM backend:

{
  "models": [
    {
      "title": "Ollama - DeepSeek Coder",
      "provider": "ollama",
      "model": "deepseek-coder-v2:16b"
    }
  ],
  "tabAutocompleteModel": {
    "title": "Ollama - StarCoder",
    "provider": "ollama",
    "model": "starcoder2:3b"
  }
}

Continue connects to whatever LLM backend you already have running. If you have Ollama set up, Continue works with it immediately. No separate deployment needed.

Continue has the simpler setup if you already have an LLM backend. Tabby is simpler if you want an all-in-one code AI server.

Performance and Resource Usage

Tabby bundles model serving:

• Requires a GPU for reasonable performance (NVIDIA recommended)
• StarCoder-1B: ~2 GB VRAM, fast completions
• StarCoder-7B: ~8 GB VRAM, better quality
• CPU mode works but completions are slow (2-5 seconds)
• Server RAM: ~1-2 GB + model size

Continue has no server footprint of its own:

• Resource usage depends entirely on your LLM backend
• With Ollama: Same as Ollama’s resource usage
• With a cloud provider (OpenAI, Anthropic): Zero local compute
• Extension itself uses minimal IDE resources

Continue is lighter because it offloads inference to an existing backend. Tabby’s all-in-one approach means you manage fewer moving parts but need dedicated GPU resources for the Tabby server.

Community and Support

Tabby: 25k+ stars. Active GitHub. Commercial TabbyML team behind it. Growing community. Enterprise features available (SSO, audit logs). Good documentation.

Continue: 25k+ stars. Active GitHub. Funded startup behind it. Large community of contributors. Extensive documentation. Active Discord. Rapid feature development with model context protocol (MCP) support.

Both have strong communities. Continue has broader LLM ecosystem integration. Tabby has better enterprise/team features.

Use Cases

Choose Tabby If…

• You want a centralized code AI server for your team
• You need repository indexing for context-aware completions
• You want usage analytics and admin controls
• You want an all-in-one solution (model serving + IDE integration)
• You need SSO/LDAP integration for enterprise deployment
• You want a dedicated GPU box serving code completions to multiple developers

Choose Continue If…

• You want maximum flexibility in choosing LLM backends
• You already have Ollama, LM Studio, or another LLM server running
• You want to use different models for different tasks (chat vs autocomplete)
• You don’t want to manage a separate server
• You want MCP (Model Context Protocol) integration
• You want to use both local and cloud models (e.g., Ollama for autocomplete, Claude for chat)
• You’re a solo developer, not managing a team

Final Verdict

Continue is the better choice for individual developers. It gives you AI code assistance with any LLM backend — local or cloud. If you already run Ollama, Continue plugs right in. The flexibility to use different models for chat vs autocomplete is a significant advantage. No server to manage, no GPU dedication required.

Tabby is the better choice for teams. A centralized Tabby server gives you admin controls, usage analytics, repository-level context, and consistent AI assistance across your development team. The all-in-one deployment is simpler than managing Ollama + Continue separately when you’re setting up AI for a team.

For a self-hoster who wants AI code assistance: install Ollama, install Continue in your IDE, and you’re done. For a team lead setting up AI for developers: deploy Tabby.

Related

• How to Self-Host Tabby
• How to Self-Host Ollama
• Ollama vs LocalAI
• Ollama vs vLLM
• Self-Hosted GitHub Copilot Alternatives
• Best Self-Hosted AI Tools
• Docker Compose Basics