Open Source Alternatives to GitHub Copilot

TL;DR

GitHub Copilot costs $10–19/month and sends your code to Microsoft/GitHub servers. The open source ecosystem now offers compelling self-hosted alternatives: Continue.dev (Apache 2.0, ~23K stars) is a VS Code/JetBrains extension that connects to any LLM — local via Ollama or cloud APIs. Tabby (Apache 2.0, ~25K stars) is a self-hosted AI coding assistant server with its own inference engine. For maximum privacy: run Ollama + Codestral on your own hardware. For best quality: Continue.dev with Claude or GPT-4 API.

Key Takeaways

Continue.dev: Apache 2.0, ~23K stars — VS Code/JetBrains plugin connecting to any LLM backend
Tabby: Apache 2.0, ~25K stars — self-hosted inference server with coding-optimized models
Ollama: MIT, ~104K stars — run LLMs locally, many coding models available
Best models for coding: Codestral (Mistral), DeepSeek Coder V2, Qwen2.5-Coder
Cost: GPU server + Ollama = free; OpenRouter API = ~$0.50–5/1M tokens
Privacy: With Ollama — your code never leaves your machine

The Copilot Alternatives Landscape

Tool	Type	License	Best For
Continue.dev	IDE plugin + LLM router	Apache 2.0	Flexibility, any LLM backend
Tabby	Self-hosted inference server	Apache 2.0	Teams, self-hosted API
Ollama	Local LLM runner	MIT	Local-first, max privacy
Cody (Sourcegraph)	IDE plugin	Apache 2.0	Large codebase context
Aider	CLI pair programmer	Apache 2.0	Terminal-based AI coding
FauxPilot	Copilot API emulator	Apache 2.0	Drop-in Copilot replacement

Option 1: Continue.dev — Most Flexible

Continue.dev is an open source IDE extension that acts as a universal LLM coding assistant frontend. Connect it to:

Ollama (local, private)
Anthropic Claude (best quality)
OpenAI GPT-4
OpenRouter (100+ models)
Tabby (self-hosted)
Any OpenAI-compatible API

Install Continue.dev

VS Code:

Extensions → Search "Continue" → Install
Or: code --install-extension Continue.continue

JetBrains (IntelliJ/PyCharm/WebStorm):

Settings → Plugins → Search "Continue" → Install

Configure with Ollama (Local, Private)

First, install Ollama and a coding model:

# Install Ollama:
curl -fsSL https://ollama.com/install.sh | sh

# Pull a coding-optimized model:
ollama pull codestral:latest         # Mistral's coding model (22B)
ollama pull deepseek-coder-v2:16b    # Excellent code completion
ollama pull qwen2.5-coder:14b        # Strong multilingual coding

# Verify:
ollama list

Configure Continue.dev (~/.continue/config.json):

{
  "models": [
    {
      "title": "Codestral (Local)",
      "provider": "ollama",
      "model": "codestral:latest",
      "apiBase": "http://localhost:11434",
      "contextLength": 32768
    },
    {
      "title": "DeepSeek Coder V2",
      "provider": "ollama",
      "model": "deepseek-coder-v2:16b",
      "apiBase": "http://localhost:11434"
    }
  ],
  "tabAutocompleteModel": {
    "title": "Qwen2.5-Coder (autocomplete)",
    "provider": "ollama",
    "model": "qwen2.5-coder:7b",
    "apiBase": "http://localhost:11434"
  },
  "embeddingsProvider": {
    "provider": "ollama",
    "model": "nomic-embed-text"
  },
  "slashCommands": [
    {"name": "edit", "description": "Edit selected code"},
    {"name": "comment", "description": "Add docstrings/comments"},
    {"name": "test", "description": "Generate unit tests"},
    {"name": "explain", "description": "Explain selected code"}
  ],
  "contextProviders": [
    {"name": "diff", "params": {}},
    {"name": "open", "params": {}},
    {"name": "terminal", "params": {}},
    {"name": "problems", "params": {}},
    {"name": "codebase", "params": {}}
  ]
}

Configure with Claude API (Best Quality)

{
  "models": [
    {
      "title": "Claude Sonnet",
      "provider": "anthropic",
      "model": "claude-sonnet-4-5",
      "apiKey": "your-anthropic-api-key"
    }
  ],
  "tabAutocompleteModel": {
    "title": "Claude Haiku (autocomplete)",
    "provider": "anthropic",
    "model": "claude-haiku-3-5",
    "apiKey": "your-anthropic-api-key"
  }
}

Using Continue.dev

Chat: Ctrl/Cmd + L — ask questions about code
Edit inline: Select code → Ctrl/Cmd + I → describe what to change
Autocomplete: Enabled by default (ghost text as you type)
Codebase context: @codebase to include your full codebase context
Slash commands: /edit, /test, /comment, /explain

Option 2: Tabby — Self-Hosted Team Server

Tabby is a self-hosted AI coding assistant server. It runs inference locally and serves multiple users on a team from one GPU server.

Docker Setup

# docker-compose.yml
version: '3.8'

services:
  tabby:
    image: tabbyml/tabby:latest
    restart: unless-stopped
    ports:
      - "8080:8080"
    volumes:
      - tabby_data:/data
    command:
      - serve
      - --device
      - cpu            # or: cuda (NVIDIA GPU), metal (Apple Silicon)
      - --model
      - TabbyML/DeepseekCoder-6.7B-instruct
      - --chat-model
      - TabbyML/Mistral-7B-Instruct-v0.2
    # For NVIDIA GPU:
    # deploy:
    #   resources:
    #     reservations:
    #       devices:
    #         - driver: nvidia
    #           count: 1
    #           capabilities: [gpu]

volumes:
  tabby_data:

docker compose up -d

# Check status:
curl http://localhost:8080/v1/health

Connect IDE to Tabby

In VS Code → Install Tabby extension → Settings:

Server URL: http://localhost:8080 (or https://tabby.yourdomain.com with Caddy)
Auth token: Generate in Tabby dashboard

tabby.yourdomain.com {
    reverse_proxy localhost:8080
}

Team Setup

Multiple developers connect their IDEs to the same Tabby server. One GPU handles everyone's completions.

Tabby Model Recommendations

Model	Size	Use Case
`TabbyML/DeepseekCoder-6.7B-instruct`	6.7B	Balanced quality/speed
`TabbyML/CodeLlama-13B`	13B	Better for complex completions
`TabbyML/DeepseekCoder-1.3B`	1.3B	Fastest, works on CPU

Model Recommendations for 2026

Best Local Models (Ollama)

Model	Parameters	Strengths	Min VRAM
Codestral	22B	Best code generation, multilingual	16GB
DeepSeek-Coder-V2	16B	Strong at complex tasks	12GB
Qwen2.5-Coder	14B	Excellent multilingual, efficient	10GB
Qwen2.5-Coder 7B	7B	Great quality/speed balance	6GB
DeepSeek-Coder-V2	8B	Best for CPU-only systems	8GB RAM

No GPU? Use Quantized Models

# 4-bit quantized — runs on CPU (slower but works):
ollama pull qwen2.5-coder:7b-instruct-q4_K_M   # ~4.5GB RAM
ollama pull deepseek-coder:6.7b-instruct-q4_0   # ~4GB RAM

API-Based (Highest Quality)

If privacy isn't the primary concern:

Provider	Model	Cost	Notes
Anthropic	claude-sonnet-4-5	~$3/1M tokens	Best overall quality
Mistral	codestral-latest	~$1/1M tokens	Best dedicated coding model
OpenRouter	Various	~$0.10–5/1M tokens	Mix models per task

Hardware Requirements

Setup	RAM	GPU	Use Case
Ollama CPU only	16GB+ RAM	None	Slow but works
Ollama + consumer GPU	8GB RAM	RTX 3060 (12GB)	7B models at good speed
Ollama + prosumer GPU	16GB RAM	RTX 4090 (24GB)	22B models fast
Tabby team server	32GB RAM	A10/A100	Multi-user enterprise

For most developers: Install Ollama locally, use Qwen2.5-Coder 7B for autocomplete (fast) and Claude/GPT-4 via Continue.dev for complex questions.

Privacy Comparison

Option	Code sent to	Privacy level
GitHub Copilot	Microsoft/GitHub	❌ None
Continue + Claude	Anthropic	⚠️ API calls
Continue + Ollama	Nobody	✅ Complete
Tabby (self-hosted)	Your server	✅ Complete
Tabby (cloud)	Tabby servers	⚠️ API calls

For maximum code privacy (proprietary code, regulated industries): Ollama + Continue.dev on your own hardware.

See all open source AI developer tools at OSSAlt.com/categories/ai-tools.

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