Continue vs Tabby vs Cody
Three open-source alternatives to GitHub Copilot that you can self-host or connect to local models. Compare their IDE support, features, and the ideal setup for developers and teams.
Continue
Open-source AI code assistant IDE extension — connect to any local or cloud LLM
Tabby
Self-hosted AI coding assistant with a standalone inference server
Cody
AI code assistant by Sourcegraph with deep codebase context and search
Feature Comparison
| Feature | Continue | Tabby | Cody |
|---|---|---|---|
| Open source | |||
| Free tier | |||
| Self-hostable | |||
| Works fully offline | |||
| Local model support | Via API | ||
| Ollama integration | |||
| VS Code extension | |||
| JetBrains extension | |||
| Neovim/Vim support | |||
| Inline code completion | |||
| Chat interface in IDE | |||
| Repository-level context | |||
| Codebase indexing | |||
| Multi-user / team support | |||
| Analytics dashboard | |||
| Multiple LLM backends | |||
| GPU required | |||
| Min RAM (server) | N/A | 8 GB | N/A |
Tool Deep Dives
Continue
Continue is an open-source IDE extension (VS Code + JetBrains) that turns any LLM into a coding assistant. You configure it with any backend — Ollama, LM Studio, Anthropic, OpenAI, or a custom API. The chat sidebar handles code explanation, refactoring, documentation, and Q&A. Autocomplete uses a fast local fill-in-the-middle model (like DeepSeek Coder or Starcoder2) for real-time suggestions with sub-100ms latency.
Continue's flexibility is its superpower — you pick the model per task. Use a fast 3B model for autocomplete and a powerful 70B model for complex refactoring. Its slash commands (/edit, /comment, /test) make common tasks instant. Continue has no backend server requirement — it talks directly to whatever API you configure.
Pros
- ✓ Connect to any LLM (Ollama, OpenAI, Anthropic)
- ✓ No backend server needed
- ✓ Chat + autocomplete in one extension
- ✓ Highly configurable
- ✓ VS Code & JetBrains
- ✓ Active open-source development
Cons
- ✗ No repository-level code understanding
- ✗ No team features or analytics
- ✗ Requires manual LLM setup
- ✗ Autocomplete quality depends on chosen model
Tabby
Tabby is a self-hosted AI coding assistant that you deploy as a server — then connect IDE extensions to it. The architecture makes it ideal for teams: everyone points their IDE at the same Tabby server which you control. Tabby uses specialized code models (CodeLlama, DeepSeek Coder, StarCoder) for high-quality autocomplete.
Tabby's admin dashboard provides usage analytics, model configuration, and user management. It supports CUDA and Apple Metal for GPU acceleration. The dedicated server model means you can scale it independently and update models without touching IDE configurations.
Pros
- ✓ Self-hosted server for team deployment
- ✓ Admin dashboard and analytics
- ✓ Optimized code completion models
- ✓ Multiple IDE plugins
- ✓ GPU acceleration support
Cons
- ✗ Requires running a separate server
- ✗ More complex setup than Continue
- ✗ No chat interface (completion only)
- ✗ Less flexible model selection
Cody (by Sourcegraph)
Cody stands apart with its deep codebase understanding powered by Sourcegraph's code intelligence platform. While Continue and Tabby understand the files you have open, Cody indexes your entire repository and uses that context to answer questions across your full codebase. "How is the authentication implemented?" becomes answerable even if you haven't opened those files.
Cody's free tier works with Claude and GPT-4 on small codebases. Enterprise plans provide dedicated infrastructure and full Sourcegraph integration. The IDE experience is polished with chat, inline commands, unit test generation, and documentation writing.
Pros
- ✓ Repository-level code understanding
- ✓ Best context window usage
- ✓ Multi-IDE support (VS Code, JetBrains, Neovim, Emacs)
- ✓ Sourcegraph code search integration
- ✓ Team and enterprise features
Cons
- ✗ Requires cloud connection (Sourcegraph)
- ✗ Not truly offline
- ✗ Enterprise features are expensive
- ✗ Less model flexibility than Continue
Which Should You Choose?
Maximum flexibility, no server, works with Ollama for full privacy. The Swiss Army knife for solo developers.
Self-hosted server gives teams centralized control, analytics, and a consistent AI coding experience.
When you need to ask questions about a large repository, Cody's indexing and Sourcegraph integration are unmatched.
Our Recommendation
Continue wins for individual developers with its unmatched flexibility and Ollama integration for fully local, private coding assistance. Tabby is the runner-up for teams needing a self-hosted server. Cody is the specialist pick for large codebases requiring deep context.
Frequently Asked Questions
Can Continue, Tabby, and Cody replace GitHub Copilot?
All three can replace Copilot for code completion and chat. Continue and Tabby work best with local models via Ollama, giving you Copilot-level functionality with full privacy. Cody by Sourcegraph adds unique repository-level context.
Which works with VS Code and JetBrains IDEs?
Continue supports both VS Code and JetBrains. Tabby has VS Code, JetBrains, and Vim plugins. Cody supports VS Code, JetBrains, Neovim, and Emacs.
Does Tabby require a server?
Yes — Tabby is a self-hosted server that your IDE extension connects to. Continue and Cody can connect directly to Ollama or other local model servers without a separate backend.
Which is best for teams?
Tabby and Cody are designed for team deployment. Tabby has built-in multi-user support and analytics. Cody's Sourcegraph backend provides enterprise code search and context.
Can I use Continue with Ollama?
Yes! Continue + Ollama is one of the most popular local AI coding setups. You configure your Ollama endpoint in Continue's settings, then use any Ollama model for both completion and chat.