Tabby vs Continue.dev vs Cline 2026: Self-Hosted Code AI

#opensource #ai #selfhosted #linux

This article was originally published on aifoss.dev

TL;DR: These three tools are fundamentally different — Tabby is a team server that runs one GPU for everyone, Continue.dev is a personal copilot inside your IDE, and Cline is an autonomous coding agent that executes tasks. Running all three simultaneously is a valid choice. The comparison you actually need is: which covers the gap Copilot leaves for your specific workflow.

	Tabby v0.32	Continue.dev v1.2	Cline v3.85
Best for	Teams sharing one GPU	Daily autocomplete + chat	Autonomous task execution
Autocomplete	✅ Centralized	✅ Per-developer	❌ None
Agent mode	❌ None	❌ None	✅ Full Plan/Act
Local models	Built-in registry	Any Ollama/LM Studio model	Any OpenAI-compatible endpoint
Setup effort	Medium (GPU server + Docker)	Low (extension + JSON config)	Low (extension + API key)
Team features	SSO, admin dashboard, analytics	Shared `.continue/` config	Shared `.clinerules` files
Hardware needed	GPU server (8 GB VRAM min)	Runs on dev machine	No compute — just a good model
License	Apache 2.0 (EE dir proprietary)	Apache 2.0	Apache 2.0

Honest take: For most developers, the answer is Continue.dev running daily with a local Ollama model for autocomplete, plus Cline when you have a complete ticket to hand off. Tabby earns its complexity only when a team needs one central server with no individual API keys in developer machines.

The Comparison That Usually Gets It Wrong

Tabby, Continue.dev, and Cline show up together in every "GitHub Copilot alternatives" roundup. They share an Apache 2.0 license, work in VS Code, and all claim to be open-source replacements for paid coding AI. That surface similarity causes a lot of people to spend time comparing things that don't compete.

Tabby's product answer is: deploy one server, plug in your team's GPU, let twenty developers share it with no API keys on individual machines. Continue.dev's answer is: give every developer full control over which model handles which request, routing fast local models for autocomplete and larger models for chat. Cline's answer is: hand me a task description, I'll read your files, write code, run tests, and loop until it's done.

These tools overlap in the sense that a hammer, a screwdriver, and a socket wrench all live in the same toolbox. Comparing them directly makes sense only when you understand which job each was designed to do.

Tabby: The Team Server

Tabby (v0.32.0, Apache 2.0, ~33.5k GitHub stars as of May 2026) is a self-hosted AI coding assistant designed to run as a shared server for a development team. The pitch is clear: one deployment, centralized GPU access, no developer on the team needs an API key in their IDE.

The core workflow is server-side. You stand up Tabby — via Docker or a native binary — on a machine with a GPU, connect it to your codebase repositories, and distribute IDE plugins to your team. Developers install the VS Code extension, JetBrains plugin, or Vim plugin, point it at the Tabby server, and get code completion without touching any configuration.

What Tabby does well:

The built-in model registry covers the practical range for code completion: StarCoder models (1B–7B), CodeGemma, CodeQwen, and Qwen2-based completion models. The 1B and 3B models are genuinely fast for completion; the 7B models are better quality but need more VRAM.

Codebase context is a real feature here. Tabby can index your Git repositories — GitHub, GitLab, or self-hosted — and use that code as retrieval context for completions. This is the kind of context awareness that makes completions useful in a real codebase rather than producing generic boilerplate.

The admin dashboard shows usage statistics, lets you manage users, configure models, and review what code was sent to the server. For teams with audit requirements, that visibility matters.

Starting Tabby with Docker (GPU):

docker run -it --gpus all \
  -p 8080:8080 \
  -v $HOME/.tabby:/data \
  tabbyml/tabby serve \
  --model TabbyML/CodeQwen-7B \
  --chat-model TabbyML/Qwen2-1.5B-Instruct

Replace the model names with whatever fits your VRAM. The completion model handles inline completions; the chat model handles the answer engine sidebar.

Tabby's rough edges:

The enterprise feature split is confusing. The Apache 2.0 license covers the core server, but the ee/ directory in the repository uses a proprietary license for features like SSO (GitHub OAuth, LDAP) and certain admin capabilities. For self-hosters, the practical question is whether you need SSO — if you do, check the current EE terms before committing to the deployment.

Tabby doesn't do agent tasks. You won't hand it a feature description and have it write the code. It's a completion and chat assistant only. And unlike Continue.dev, you can't reroute individual requests to different model providers — it's one server, one model config, for everyone.

Continue.dev: The Personal Copilot

Continue.dev (VS Code extension v1.2.22, Apache 2.0, ~33.4k GitHub stars) is an IDE extension that sits between your editor and any model backend you choose. The distinctive feature is routing: you configure different models for different tasks, and every configuration lives in a JSON file that can be committed to your repository.

For the developer who wants something close to Copilot but fully under their control, this is the right starting point. Install the extension, add an Ollama endpoint, and you have inline completions and a chat sidebar within minutes.

Setting up model routing in Continue.dev:

{
  "models": [
    {
      "title": "Qwen2.5-Coder 14B (Chat)",
      "provider": "ollama",
      "model": "qwen2.5-coder:14b"
    }
  ],
  "tabAutocompleteModel": {
    "title": "Qwen2.5-Coder 7B (Autocomplete)",
    "provider": "ollama",
    "model": "qwen2.5-coder:7b"
  }
}

This config runs a fast 7B model for every keystroke (autocomplete fires constantly and needs low latency) and a larger, smarter 14B model for chat queries you type intentionally. Both run locally through Ollama. Zero API cost.

The codebase context works through the @codebase command in chat — it embeds and searches your local repository to answer questions about your own code. It's less polished than GitHub Copilot's context awareness, but it runs entirely offline.

What Continue.dev does well:

Model flexibility is the headline. Continue.dev works with Ollama, LM Studio, OpenAI, Anthropic, Google Gemini, AWS Bedrock, and any OpenAI-compatible endpoint. You can switch providers per task type. MCP (Model Context Protocol) server support means you can inject external tool calls — web search, database queries, custom APIs — into the chat context.

The config-as-code approach is genuinely useful for teams. Commit a .continue/config.json to your repository and every team member inherits the same model configuration, prompt templates, and context providers.

Continue.dev's rough edges:

No agent mode. You can ask Continue to write code in the chat panel and apply it to a file, but it can't autonomously edit multiple files, run terminal commands, and iterate. For that, you need Cline alongside it.

Autocomplete quality depends heavily on your local model choice. The qwen2.5-coder:7b model is genuinely good for completion on modern hardware; the 3B model is faster but noticeably worse on complex TypeScript. On CPU-only machines without a discrete GPU, expect latency that makes inline completion feel sluggish.

For a deeper look at Continue.dev's setup and features, see the Continue.dev review.