Developers who want AI-assisted coding without sending their code to cloud services have three leading options in 2026: Continue, Tabby, and Aider. Each takes a different approach to integrating local LLMs into the development workflow — Continue is an IDE extension for inline completions and chat, Tabby is a self-hosted code completion server, and Aider is a terminal-based AI pair programmer. This comparison examines which tool best fits different development styles, team configurations, and privacy requirements.
Quick Comparison
| Feature | Continue | Tabby | Aider |
|---|---|---|---|
| Type | IDE extension | Self-hosted completion server | Terminal AI pair programmer |
| Interface | VS Code / JetBrains sidebar + inline | IDE plugin + server dashboard | Terminal / CLI |
| Inline completions | Yes (tab-to-accept) | Yes (tab-to-accept) | No (chat-based edits) |
| Chat interface | Yes (IDE sidebar) | Limited | Yes (terminal) |
| Multi-file editing | Yes (with context) | No (single-file completions) | Yes (primary strength) |
| Git integration | No | No | Yes (auto-commits changes) |
| Code indexing | Yes (workspace context) | Yes (repository-level) | Yes (repository map) |
| Local model support | Ollama, llama.cpp, LM Studio, any OpenAI-compatible | Custom models, Ollama | Ollama, any OpenAI-compatible |
| Cloud model support | OpenAI, Anthropic, Google, Azure | OpenAI (optional) | OpenAI, Anthropic, Google, many others |
| IDE support | VS Code, JetBrains | VS Code, JetBrains, Vim/Neovim | Any editor (terminal-based) |
| Self-hosted | Extension only (no server needed) | Yes (server + extension) | No server needed (CLI tool) |
| Team features | Configuration sharing | Admin dashboard, usage analytics | None (single-user) |
| License | Apache 2.0 | Apache 2.0 (with Enterprise tier) | Apache 2.0 |
| Setup time | 5-10 minutes | 15-30 minutes | 5 minutes |
IDE Integration
Continue
Continue provides the deepest IDE integration among the three tools. As a VS Code extension (with JetBrains support), it embeds directly into the development environment with:
- Inline completions: Ghost text suggestions that appear as you type, accepted with Tab — the same UX as GitHub Copilot
- Chat sidebar: A conversation panel within the IDE where you can ask questions, request code generation, and discuss your codebase
- Context providers: Configure what context the model receives — open files, selected code, terminal output, documentation, Git diffs, and more
- Slash commands: Quick actions like
/editfor inline editing,/commentfor adding comments,/testfor generating tests - Codebase indexing: Local embeddings of your workspace for context-aware suggestions
Continue’s integration feels native to the IDE. You can highlight code and ask questions about it, request inline edits, or have a conversation about architecture — all without leaving your editor.
Tabby
Tabby takes a server-client approach. The Tabby server runs as a separate process (or Docker container) and provides code completions via a language-server-compatible protocol. IDE extensions for VS Code, JetBrains, and Vim/Neovim connect to the server.
The completion experience is focused on inline code suggestions — Tabby excels at predicting the next few lines of code based on the surrounding context. The server indexes your repository to provide context-aware completions that reference patterns, functions, and conventions from your codebase.
Tabby’s chat capabilities are more limited than Continue’s. The focus is on fast, accurate inline completions rather than conversational code assistance.
Aider
Aider does not integrate into an IDE at all. It runs in the terminal alongside your editor of choice. You tell Aider which files to work with, describe the changes you want in natural language, and Aider edits the files directly. After each change, Aider creates a Git commit with a descriptive message.
This approach means Aider works with any editor — VS Code, Neovim, Emacs, Sublime Text, or even plain nano. There is no plugin to install, no extension to configure. You open a terminal, run aider, and start describing changes.
The tradeoff is that Aider does not provide real-time inline suggestions as you type. It is a conversation-driven tool for deliberate changes, not a passive autocomplete assistant.
Model Support
Continue
Continue supports the broadest range of model providers through its configuration file (config.json or config.yaml). You can configure:
- Local: Ollama, LM Studio, llama.cpp server, any OpenAI-compatible endpoint
- Cloud: OpenAI, Anthropic Claude, Google Gemini, Azure OpenAI, AWS Bedrock, Mistral, Cohere, Together, Groq, and more
Continue allows configuring different models for different tasks — one model for inline completions (optimized for speed) and a different model for chat (optimized for quality). This flexibility lets you use a small, fast model like Qwen2.5-Coder 1.5B for completions and a larger model like Qwen2.5-Coder 32B for chat.
Tabby
Tabby supports loading models through its own model configuration system. It works with:
- Local: Custom GGUF models, models from the Tabby model registry, Ollama
- Specialized code models: StarCoder, CodeLlama, DeepSeek-Coder, and other code-specific models
Tabby’s model support is more focused on code-specialized models. The server handles model loading, inference, and context management, so switching models requires restarting the server with a different configuration.
Aider
Aider connects to any model provider through the LiteLLM library, which provides a unified interface to dozens of providers:
- Local: Ollama, LM Studio, any OpenAI-compatible endpoint
- Cloud: OpenAI, Anthropic, Google, Azure, AWS, Mistral, Groq, DeepSeek, and many more
Aider maintains a leaderboard of models ranked by code editing performance, which helps users choose the best model for their budget and privacy requirements. The leaderboard tests models on real coding tasks and provides objective quality scores.
Code Quality
Code quality with local models depends primarily on the model, not the tool. However, the tools differ in how effectively they use the model.
Continue
Continue’s code quality for inline completions depends heavily on the completion model. Small models (1-3B parameters) provide fast but sometimes inaccurate completions. Larger models (7B+) provide better completions but with noticeable latency. The fill-in-the-middle (FIM) capability of code models is well-utilized by Continue’s completion engine.
For chat-based code generation, Continue’s context providers help the model understand your codebase. Providing relevant context (open files, selected code, documentation) significantly improves the quality of generated code.
Tabby
Tabby’s strength is repository-level context. By indexing your codebase, Tabby provides completions that are aware of your project’s conventions, function signatures, and patterns. This repository awareness improves completion accuracy compared to tools that only see the current file.
Tabby’s code completion quality is competitive with Continue when both use the same underlying model, with Tabby sometimes edging ahead on project-specific completions thanks to its deeper repository indexing.
Aider
Aider excels at complex, multi-file changes. Its diff-based editing approach (sending the model a description of changes and applying the returned diffs) is more reliable for large edits than approaches that regenerate entire file contents. Aider’s repository map feature gives the model an overview of the codebase structure, helping it make changes that are consistent with the existing architecture.
For multi-file refactoring, adding new features that span multiple files, or complex bug fixes, Aider typically produces higher-quality results than inline completion tools because the conversational workflow allows for clarification, iteration, and review.
Team Features
Continue
Continue supports team use through configuration sharing. Teams can maintain a shared config.json that standardizes model endpoints, context providers, and slash commands. However, Continue runs client-side with no central server, so there is no usage analytics or centralized management.
Tabby
Tabby has the strongest team features. The self-hosted server provides:
- Admin dashboard: User management, model configuration, and system monitoring
- Usage analytics: Track completion accept rates, user activity, and model performance
- Access control: Per-user or per-team model access
- Enterprise features: SSO, audit logs, and compliance features in the enterprise tier
For teams deploying a shared code assistant, Tabby’s centralized architecture makes administration and monitoring straightforward.
Aider
Aider is a single-user tool with no team features. Each developer runs their own instance with their own model configuration. For teams, Aider is viable if each developer manages their own setup, but there is no centralized administration, shared configuration, or usage tracking.
Setup Ease
Continue
Continue setup involves:
- Install the VS Code extension from the marketplace
- Edit the configuration file to add an Ollama model endpoint
- Start using completions and chat
Total time: 5-10 minutes. The configuration file is well-documented, and the extension provides a getting-started walkthrough. If Ollama is already running, Continue detects it automatically.
Aider
Aider setup involves:
pip install aider-chat(orpipx install aider-chat)- Set the model endpoint environment variable (e.g.,
OLLAMA_API_BASE) - Navigate to your project and run
aider
Total time: 5 minutes. Aider’s CLI approach means there is nothing to configure in your IDE. The tradeoff is that you manage the terminal session separately from your editor.
Tabby
Tabby setup involves:
- Deploy the Tabby server (Docker recommended):
docker run -p 8080:8080 tabbyml/tabby serve --model StarCoder-1B - Install the IDE extension
- Configure the extension to point at the Tabby server
- Optionally configure repository indexing
Total time: 15-30 minutes. The server deployment step adds complexity but provides benefits (centralized management, repository indexing, team features). Docker makes it reproducible, but GPU passthrough configuration can add friction.
The Bottom Line
Choose Continue if you want the closest local alternative to GitHub Copilot. It provides inline completions and chat within VS Code or JetBrains, supports the widest range of model providers, and sets up in minutes. It is the best all-around choice for individual developers.
Choose Tabby if you need a team-oriented code assistant with centralized management, usage analytics, and repository-level context. The server-client architecture adds setup complexity but provides features that Continue and Aider cannot match for team deployments.
Choose Aider if your workflow involves complex, multi-file changes and you prefer a conversational approach to coding. Aider’s strength is deliberate, high-quality edits with Git integration — not real-time inline completions. It is the best tool for refactoring, feature implementation, and bug fixing across large codebases.
Many developers use more than one tool: Continue for daily inline completions and Aider for complex changes. The tools complement rather than compete, because they serve different modes of development.