I Stopped Paying $240/Year for AI Code Completion — These 4 Free Tools Do 90% of It

说明与用途

作者用免费的本地工具堆栈（Continue、Tabby、MonkeyCode、Dify）替代了付费的 GitHub Copilot，获得了 90% 的功能、降低了延迟并且零成本。

问题

Developers paying for paid AI coding assistants like GitHub Copilot may be able to replace most of its functionality with free, locally-hosted tools, reducing cost and improving privacy.

结果

The free stack achieved ~90% of the functionality of GitHub Copilot, with lower latency and zero cost, while maintaining compliance by keeping all data local.

工作流

• 1. Install Tabby via Docker: docker run -it --gpus all -p 8080:8080 -v $HOME/.tabby:/data tabbyml/tabby serve --model Qwen2.5-Coder-1.5B --device cuda
• 2. Point VS Code Tabby extension to localhost:8080
• 3. Configure Continue with local Ollama models via ~/.continue/config.yaml
• 4. Use Continue for chat, inline edits, and autocomplete with local models
• 5. Use MonkeyCode web interface for agentic multi-file tasks
• 6. Use Dify with Docker Compose for RAG over internal docs and codebase

证据

The free stack covers ~90% of what I used Copilot for, and it's not the 90% you'd expect.

来源

Suggestion latency: ~180ms (Copilot averaged ~300ms for me through their API)

来源

Zero tokens sent anywhere.

来源