Stop paying $20/mo for GitHub Copilot or Cursor. Set up Ollama + Continue.dev in minutes and get a private, self-hosted AI coding assistant with zero cloud fees, unlimited usage, and complete code privacy.
Cloud AI coding tools are expensive, invasive, and unpredictable. There's a better way — local AI with zero subscriptions.
GitHub Copilot, Cursor, and Windsurf charge $10–$40/month per developer. Replace them with a free self-hosted LLM that runs on your own machine.
Cloud AI tools throttle your completions during peak hours. Local AI runs at full speed 24/7 — no queues, no outages, no slowdowns.
Cloud AI tools send your code to remote servers. With a local LLM, every prompt, completion, and context stays 100% on your hardware.
Cloud providers can change pricing or shut down at any time. Own your full AI stack — swap models freely, no dependencies on any single provider.
Work on planes, in remote locations, or behind strict firewalls. Local AI coding assistants require no internet after the initial model download.
Fine-tune models on your own codebase, adjust system prompts, and integrate with any IDE. Something cloud providers simply don't allow.
Use Ollama to serve open-source models like DeepSeek Coder or CodeLlama, and connect them to Continue.dev in VS Code — a complete Copilot replacement at $0/month.
Run DeepSeek Coder, CodeLlama, Qwen2.5-Coder, or Mistral locally with a single command. Ollama handles model downloads, GPU acceleration, and API serving automatically.
Continue.dev is the open-source GitHub Copilot alternative for VS Code and JetBrains. Connect it to your local Ollama instance for inline completions, chat, and agentic edits.
Every prompt and completion runs on your hardware. No code is sent to OpenAI, GitHub, or any third-party server — ideal for proprietary, enterprise, or sensitive projects.
No token limits, no rate caps, no quota resets. Generate as many code completions as you need, whenever you need them — limited only by your hardware.
Swap between DeepSeek Coder, CodeLlama, Phi-3, Mistral, and more. No single-model lock-in. Choose the best model for each task and update freely.
Go beyond what Copilot offers — fine-tune open-source models on your own repositories to get completions that truly understand your coding style and architecture.
GitHub Copilot costs $10–$19/mo. Cursor costs $20/mo. Teams pay hundreds more. A local AI setup pays for itself in weeks.
Pay per API call or token usage. Costs scale with your usage.
Providers can raise prices at any time without notice.
Rate limits and quotas can slow down your workflow.
Hard to switch providers once you're invested.
Additional costs for storage, API calls, and features.
Estimated monthly cost: $50-$500+ depending on usage
Hardware costs are one-time. No recurring monthly fees.
Once set up, your costs remain the same forever.
Use as much as you want with no rate limits.
Complete control over your tools and data.
Know exactly what you're paying for.
Hardware cost: $300-$2000 one-time investment
Savings: $600-$6000+ per year
You don't need a supercomputer. Most developers already have enough hardware to replace GitHub Copilot with a local LLM today.
Get the most out of your Ollama + Continue.dev setup with these tips for performance, security, and workflow efficiency.
Start with DeepSeek Coder 6.7B or CodeLlama 7B for fast completions. Upgrade to 13B+ models when you need deeper code understanding and refactoring capabilities.
Set your context window size, tab completion model, and chat model separately in Continue.dev's config.json for optimal speed and quality across different tasks.
Always ensure Ollama uses your GPU. Run ollama run codellama and check GPU utilization. With CUDA enabled, inference is 5–10x faster than CPU-only.
Never accidentally route completions to a cloud API key. Audit your Continue.dev config regularly to confirm the provider is set to Ollama, not OpenAI or Anthropic.
Open-source coding models improve rapidly. Run ollama pull deepseek-coder-v2 monthly to get the latest improvements in code completion and reasoning.
Add your project's README and key files to Continue.dev's context. The more context the local model has about your stack, the more relevant its completions become.
Use htop or Task Manager to watch RAM and VRAM while running local models. Quantized (Q4/Q5) models use 50% less memory with minimal quality loss.
Run Ollama on a shared local server so your whole team can use the same self-hosted AI coding assistant — replacing per-seat Copilot subscriptions entirely.
Thousands of developers have already switched from GitHub Copilot to local AI. Join the community to share setups, get help, and stay ahead of new open-source models.
Share your Ollama setups, Continue.dev configs, and model benchmarks. Get help migrating from GitHub Copilot or Cursor to a fully self-hosted AI coding workflow.