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.

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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.

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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.

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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.

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Cline: The Autonomous Agent

Cline (v3.85.0, Apache 2.0, ~62.4k GitHub stars, 5M+ VS Code installs) is not a code completion tool. It’s an agent. You give it a task in plain language, it reads your repository, writes code, runs commands in the terminal, checks error output, and loops until it thinks the task is done. You approve or reject each step.

The Plan/Act mode separation is the clearest expression of its philosophy. In Plan mode, Cline analyzes the codebase and lays out what changes it would make — without touching anything. In Act mode, it executes, pausing at each step for your approval. The approval model means you stay in control of what actually lands in your codebase.

What Cline does well:

Multi-file task handling is where Cline pulls ahead of everything else in this comparison. Give it “extract the authentication logic from auth.ts into a separate AuthService class and update all imports” and it will read every relevant file, write the new class, update the imports, and run your test suite to check for regressions. That’s not something Continue.dev or Tabby attempts.

Provider support is the widest in this comparison. Cline works with every cloud provider — Anthropic, OpenAI, Google, AWS Bedrock, Azure — as well as any Ollama or LM Studio endpoint. With 62.4k GitHub stars and 5M+ installs, it has a much larger community producing guides and .clinerules templates for specific frameworks.

Cline’s rough edges:

No autocomplete. This is the most common misunderstanding about Cline. If you want suggestions appearing as ghost text while you type, Cline isn’t your tool.

Local model quality matters more here than anywhere. An agent loop that misunderstands a requirement doesn’t just give you a wrong completion — it edits multiple files incorrectly. Community experience puts 14B models at roughly workable for routine refactors; 32B+ models for complex multi-file tasks. A 7B model is not reliable enough for autonomous task execution.

Token cost per task scales up fast. Cline sends your full directory structure, file contents, system prompt, tool definitions, and conversation history on each agent step. With a cloud model like Claude Sonnet 4.6, a medium-complexity task runs $0.05–$0.30 depending on codebase size. With a local model, the cost is electricity — but the timeout window needs to be increased (90–120 seconds for a 14B model, 180+ for 32B+).

For Cline setup details including local model configuration, see the Cline setup guide.

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Hardware Requirements

This is where the tools diverge most sharply in what they ask of you.

Tabby requires a GPU server. The minimum practical setup is a machine with 8 GB VRAM for a 7B completion model. For a small team, an RTX 4090 (24 GB VRAM) gives you enough headroom to run a 7B completion model and a 13B chat model simultaneously. Apple Silicon — a Mac Mini M4 Pro or Mac Studio — works well with Metal acceleration and has enough unified memory for larger models. If you don’t own a GPU and want to evaluate Tabby before buying hardware, RunPod rents GPU instances by the hour.

Continue.dev needs no dedicated server. It connects to whatever model backend you point it at. If you run Ollama on your development machine, Continue.dev uses that. If your team has a shared Ollama instance, you can point everyone there. The extension itself is lightweight.

Cline has no compute requirements of its own — it’s a VS Code extension that calls an external API. The question is which model you’re calling. With cloud models, any developer machine works. With local models, you need Ollama running somewhere with enough VRAM for a reliable 14B+ model.

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Autocomplete speed benchmarks by hardware

Inline autocomplete latency determines whether ghost-text suggestions feel fluid or feel like lag. The practical threshold: suggestions that appear in under 350ms feel instant to most typists. Above 500ms, the IDE starts to feel sluggish. Above 1 second, the experience is worse than no autocomplete at all.

These benchmarks apply to Continue.dev with qwen2.5-coder Q4_K_M via Ollama — the most common self-hosted autocomplete stack in 2026. Tabby running CodeQwen-7B adds roughly 10–15% overhead from its server layer compared to a local Ollama endpoint, so subtract a proportionate margin for Tabby numbers. Cline has no autocomplete to benchmark.

qwen2.5-coder 7B Q4_K_M (recommended autocomplete model):

Hardware	Tokens/sec	Latency for 5-token completion	Usability
RTX 4090 24GB	100–130 tok/s	~40–50ms	Imperceptible
RTX 3090 24GB	75–95 tok/s	~55–65ms	Imperceptible
RTX 3060 12GB	~42 tok/s	~120ms	Good
Apple M3 Pro (via MLX)	~30 tok/s	~165ms	Good
Apple M2 (via MLX)	~22 tok/s	~225ms	Acceptable
CPU only (Ryzen 9 7950X)	~4 tok/s	~1,250ms	Too slow

qwen2.5-coder 1.5B Q4_K_M (fast, lower quality):

Hardware	Tokens/sec	Latency for 5-token completion
RTX 3060 12GB	~110 tok/s	~45ms
Apple M2 (via MLX)	~60 tok/s	~85ms
CPU only	~15 tok/s	~333ms

The 1.5B model is worth considering on Apple Silicon or modest NVIDIA cards where the 7B model’s 225ms+ latency starts feeling sluggish. Completion quality drops noticeably on complex TypeScript generics and multi-file refactors, but for routine single-line completions the quality difference is small.

For Tabby: Running CodeQwen-7B through Tabby’s server adds a network round-trip and model-serving overhead. On a local Gigabit network, this adds 15–30ms. Over a slower LAN or when the server is under load from multiple developers, latency increases. For a single developer, local Ollama through Continue.dev will always be faster than a Tabby server on the same machine due to the eliminated HTTP layer.

For hardware sizing if you’re building a team Tabby server, the GPU selection guide for local AI at runaihome.com maps VRAM requirements to team sizes. Hardware that can sustain sub-350ms autocomplete for 3–5 concurrent developers generally needs a minimum of an RTX 4090 24GB or Apple M2 Ultra with 192GB unified memory.

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When NOT to Use Each Tool

Don’t use Tabby if:

• You’re a solo developer — the team management overhead is not worth it for one person
• You don’t have a dedicated GPU machine — CPU inference is too slow for the real-time completion use case
• You want agent-style task execution — Tabby doesn’t do this
• Your team already routes through Ollama — Continue.dev handles that without the server setup

Don’t use Continue.dev if:

• You need centralized logging and usage analytics across your team — Continue.dev has no server component
• You want autonomous multi-file task execution — this is Cline’s job, not Continue.dev’s
• You’re on a very resource-constrained machine — lightweight autocomplete models still need several GB of RAM

Don’t use Cline if:

• You want Copilot-style inline completions — Cline has no autocomplete feature
• You’re using a 7B local model for agent tasks — the quality gap causes failed multi-file operations
• You want centralized team deployment — Cline is per-developer, per-API-key

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The Full Feature Matrix

Feature	Tabby v0.32	Continue.dev v1.2	Cline v3.85
Inline autocomplete	✅	✅	❌
Chat sidebar	✅	✅	✅ (task mode)
Autonomous file edits	❌	❌	✅
Terminal command execution	❌	❌	✅
Plan/Act agent mode	❌	❌	✅
VS Code	✅	✅	✅
JetBrains	✅	✅	✅
Vim/Neovim	✅	❌	✅
Ollama support	✅ (registry)	✅ (any model)	✅ (any model)
Cloud API support	❌ (self-hosted only)	✅	✅
Codebase indexing	✅ (Git repos)	✅ (@codebase)	✅ (per task)
MCP tool support	❌	✅	✅
Team admin dashboard	✅	❌	❌
Usage analytics	✅	❌	❌
SSO / LDAP	✅ (EE license)	❌	❌
Per-developer config	❌	✅	✅
CPU-only inference	Slow	✅ (with small models)	✅ (no local compute)
GitHub stars (May 2026)	~33.5k	~33.4k	~62.4k

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The Verdict

These tools are complements, not competitors.

Tabby makes sense when you have a team that needs zero individual API keys, wants centralized control over which model runs, and has a GPU server to dedicate to the deployment. It’s also the right answer for teams in regulated environments where code can’t leave your network at all. If you’re a solo developer, skip it.

Continue.dev is the best daily-use coding assistant for individual developers who want Copilot-quality autocomplete on local or cloud models without paying a subscription. The model routing is genuinely powerful, and the VS Code + JetBrains coverage makes it the widest-reach option. Start here if you want an always-on coding assistant.

Cline is the right tool when you have a well-defined task to hand off — a refactor, a feature to scaffold, a migration to execute. Use it alongside Continue.dev: Continue for the constant stream of completions while you type, Cline when you have something worth delegating. With a 14B+ local model or a cloud API, the combination covers most of what GitHub Copilot + Copilot Chat offers, for free.

For the agentic coding space more broadly — including Aider as a CLI alternative — see the Aider review and the complete Continue.dev vs Cline vs Aider comparison.

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Frequently Asked Questions

Can I run Tabby, Continue.dev, and Cline together? Yes, and for teams this is often the best setup. Tabby provides the shared autocomplete server; Continue.dev’s VS Code extension can point at the Tabby API endpoint instead of Ollama; Cline handles autonomous task execution separately with its own model backend. The three tools don’t conflict.

Which one works best with a 7B local model? Continue.dev. The qwen2.5-coder:7b model is fast enough for inline autocomplete on recent hardware and produces useful completions. Cline at 7B is unreliable for multi-file agent tasks — expect incomplete or incorrect edits. Tabby at 7B works for completion but is overkill to run a whole server for a single developer.

Does Cline really have no autocomplete? Correct. Cline is an agent, not a completion assistant. It doesn’t insert text as you type. If inline completions are what you want, install Continue.dev alongside Cline — they don’t conflict.

How much does running these tools cost if I avoid cloud APIs? Zero API cost if you run local models through Ollama. Continue.dev with a local 7B model costs only electricity. Cline with local models has the same zero API cost, though the token volume in agent loops means you’ll want a fast local GPU rather than CPU inference. Tabby requires a dedicated GPU server — the ongoing cost is electricity (roughly $5–15/month for a home server, depending on GPU TDP and local rates).

Is Tabby’s enterprise edition actually open source? Not entirely. The core Tabby server is Apache 2.0. The ee/ directory in the repository uses a proprietary license that covers advanced admin features including SSO and LDAP. For most self-hosters, the Apache 2.0 features are sufficient. Review the ee/LICENSE file if SSO is a requirement before committing to the deployment.

Should I use Qwen3-Coder instead of qwen2.5-coder now? For chat and agent work, yes — if your hardware can run it. The community recommendation in 2026 has shifted from qwen2.5-coder toward the Qwen3 family, with Qwen3-14B or Qwen3-30B treated as the quality sweet spot for coding and the smaller Qwen3-Coder-Next (3B active parameters) running comfortably on 16GB. Qwen3’s reasoning ability is the real upgrade for multi-step agent tasks in Cline, where qwen2.5-coder’s weaker reasoning shows up as failed multi-file edits. For pure inline autocomplete in Continue.dev or Tabby, the gap is smaller: qwen2.5-coder remains a perfectly good, lower-latency choice on modest GPUs, and the 1.5B FIM build is still one of the fastest options for tab completion. The practical rule: use the largest Qwen3 model your VRAM allows for chat/agent, and keep a small fast model (1.5B–3B) for autocomplete.

Which of these tools best replaces GitHub Copilot’s paid features? Continue.dev plus Cline together. Continue.dev covers Copilot’s inline completions and chat sidebar on local or cloud models, and Cline covers the agent-mode multi-file task execution that GitHub Copilot now meters with AI Credits. Running both with a local 14B+ model gives you most of Copilot Pro’s day-to-day capability at zero API cost — relevant now that Copilot’s agent mode bills per token. Tabby replaces a different piece: it’s the answer when a whole team needs shared completions with no individual API keys, not a per-developer Copilot substitute.

What’s the cheapest hardware that runs this stack well? For a solo developer, a 12GB card like an RTX 3060 handles qwen2.5-coder 7B autocomplete at usable latency (~120ms) and can run a small chat model. For comfortable headroom — fast 7B autocomplete plus a 14B+ chat/agent model — a 24GB card (used RTX 3090 or RTX 4090) or an Apple Silicon machine with 32GB+ unified memory is the practical floor. CPU-only inference works for the smallest models but is too slow for fluid autocomplete. See the budget GPU guidance for local LLMs at runaihome.com for VRAM-to-model-size mapping.

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Sources

• Tabby GitHub repository — version, license, hardware requirements
• Tabby v0.32.0 release notes — latest version and features
• Continue.dev GitHub repository — version v1.2.22-vscode, license, stars
• Cline GitHub repository — version v3.85.0, license, installs
• Tabby hardware FAQ — Tabby ML — VRAM requirements for code completion models
• Tabby Docker Hub image — Docker deployment reference
• Continue.dev Ollama integration guide — model routing configuration

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Recommended Gear

• RTX 4090 — 24 GB VRAM; runs a 7B completion model and 13B chat model simultaneously for Tabby
• Mac Mini M4 Pro — unified memory architecture handles larger models than equivalent VRAM NVIDIA GPUs; Metal acceleration works with Tabby