Self-Hosted GitHub Copilot Alternative: Code with Ollama for Free

GitHub Copilot is ten dollars a month and your code goes to a third party. Both are fine until they're not. If you write proprietary code, work in a regulated industry, or just don't want to ship every keystroke to Microsoft, there's a free, local alternative that runs entirely on your laptop.

This post walks through the setup. End to end, twenty minutes.

The stack

• Ollama — the local model runner.
• Continue.dev — a VS Code extension that talks to Ollama.
• A code-focused LLM — qwen2.5-coder:7b for speed, qwen2.5-coder:32b for quality.

That's it. No API keys, no monthly bill, no telemetry.

Why this actually works in 2026

Local code models have closed the gap. Qwen2.5-Coder 32B benchmarks within five points of Claude Sonnet on HumanEval. The 7B variant is fast enough to autocomplete in real time on a 16 GB MacBook M2. DeepSeek-Coder-V2 and CodeLlama 70B are also strong choices if you have more RAM.

The key shift: you no longer need a cloud GPU farm to get usable AI assistance.

Install Ollama

# macOS / Linux
curl -fsSL https://ollama.com/install.sh | sh

# Windows: download the installer from ollama.com

Pull the code model:

ollama pull qwen2.5-coder:7b      # 4 GB, fast
ollama pull qwen2.5-coder:32b     # 19 GB, slower but smarter

Verify it's running:

ollama run qwen2.5-coder:7b "write a TypeScript debounce function"

It should respond in one to three seconds.

Install Continue

In VS Code, install the extension continue.continue.

Open Continue's settings (Cmd+L on macOS, then click the gear icon) and edit your ~/.continue/config.json to point at Ollama:

{
  "models": [
    {
      "title": "Qwen 2.5 Coder 7B",
      "provider": "ollama",
      "model": "qwen2.5-coder:7b",
      "apiBase": "http://localhost:11434"
    }
  ],
  "tabAutocompleteModel": {
    "title": "Tab Autocomplete",
    "provider": "ollama",
    "model": "qwen2.5-coder:7b",
    "apiBase": "http://localhost:11434"
  },
  "embeddingsProvider": {
    "provider": "ollama",
    "model": "nomic-embed-text"
  }
}

Pull the embedding model so codebase indexing works:

ollama pull nomic-embed-text

Reload VS Code. Open any TypeScript file and start typing. You should see grey ghost-text suggestions, just like Copilot.

Test it like a real user

Hit Cmd+I and ask:

Write a function that debounces a callback. Use TypeScript generics.

You should see a response in two to four seconds. The function should be correct on first attempt.

For inline edits, select code and hit Cmd+I:

Refactor this to use early returns.

The diff appears inline. Accept or reject with one keystroke.

Latency comparison

The 7B model is the right default for most flows. Bring out the 32B model when you're doing architecture work or asking for explanations.

What you actually give up

I don't want to oversell this. The 7B local model misses subtle bugs that Copilot's frontier model catches — null-check edge cases, Promise.all vs Promise.allSettled distinctions, that kind of thing. Multi-file context is also weaker. Continue indexes your repo locally, but it's not at the level of Copilot's whole-workspace awareness.

For senior engineers writing performance-sensitive code at the limit of the language, Copilot is still better. For everyone else doing 80 percent of normal day-to-day work, local Ollama is indistinguishable in quality and zero in cost.

What you save

A hundred and twenty dollars a year, plus the value of your code never leaving your machine. If you ship in a regulated industry where data sovereignty matters — health, finance, defense, legal — this is the difference between "no AI assistance allowed" and "AI assistance with a full local audit trail." That trade rarely makes sense at the cost of a single subscription.

What's next

If this works for you, the next post in this series goes deeper: comparing Ollama against LM Studio and Jan, the two other serious local AI runners. Different tradeoffs, different best-fits. Worth knowing before you commit a tool to your daily flow.

For now, you have a working local Copilot. Happy not-paying.