If you have ever used Claude Code — Anthropic’s AI coding assistant that runs in your terminal — you know how powerful it is. It generates code, refactors files, fixes bugs, and builds entire projects from a single prompt. But every interaction costs API tokens, and the bills add up fast. A heavy coding session can easily burn through $20–$50 in a single day.
What if you could get 80–90% of that power for free, running entirely on your own machine, with no API key and no data leaving your computer? That is exactly what Ollama + Claude Code lets you do. One command connects Claude Code to a local AI model, and you are coding with an AI assistant that costs nothing and keeps your code 100% private.
The API Cost Problem
Claude Code is one of the best AI coding tools available, but it has a real cost problem:
- Per-token pricing — Every prompt and response is billed. A complex refactoring across multiple files can cost $2–$5 in a single interaction.
- Monthly bills creep up — Active developers routinely spend $50–$200/month on API calls.
- Budget anxiety — You hesitate before sending a prompt because you know it costs money. That friction kills productivity.
- Privacy concerns — Your proprietary code is sent to Anthropic’s servers. Many companies restrict this for compliance reasons.
The solution: Run a local model through Ollama and connect it to Claude Code. Zero per-token cost, zero data leaving your machine, zero API key required.
How It Works — Architecture
The setup is simple. Two components, one command to connect them:
- Ollama — Runs a local LLM (like Qwen2.5-Coder) on your machine, exposing an OpenAI-compatible API endpoint.
- Claude Code — Instead of calling the Anthropic API, you point it at Ollama’s local endpoint. Claude Code sends prompts to the local model and receives responses — the same interface, just a different backend.
No data leaves your computer. No API key is needed. No internet connection is required after the model is downloaded.
What Is Ollama?
Ollama is an open-source tool that lets you run large language models locally with a single command. It handles model downloading, GPU/CPU optimization, and serving an API endpoint — all automatically. You do not need to configure CUDA, PyTorch, or any ML infrastructure.
Think of Ollama as Docker for AI models — it pulls, runs, and manages models the way Docker manages containers.
Key Facts
- Runs on macOS, Linux, and Windows
- Supports 100+ models including Llama 3, Qwen2.5, DeepSeek, Mistral, Phi, and more
- Exposes an OpenAI-compatible API on
localhost:11434 - Models run entirely on your hardware — 100% offline after download
- Free and open-source (MIT license)
What Is Claude Code?
Claude Code is Anthropic’s official CLI tool that brings AI-powered coding assistance directly into your terminal. Unlike ChatGPT or the Claude web interface, Claude Code operates on your actual codebase — it can read files, write files, run commands, and execute multi-step coding workflows.
What makes Claude Code special compared to a chat interface:
- Works on real projects — It reads your actual files, understands your codebase structure, and makes targeted edits.
- Multi-file operations — Refactor across 10 files in one prompt. It understands dependencies and imports.
- Terminal integration — It can run tests, install packages, execute builds, and read the output.
- Agentic behavior — It plans, executes, verifies, and iterates on complex tasks without hand-holding.
The catch: by default, Claude Code requires an Anthropic API key and charges per token. That is where Ollama changes the game.
Step 1 — Install Ollama
Download Ollama from ollama.com/download for your operating system.
Or install from the command line:
macOS / Linux
curl -fsSL https://ollama.com/install.sh | shWindows (PowerShell)
irm https://ollama.com/install.ps1 | iexVerify the installation:
ollama --versionStep 2 — Pull a Coding Model
Not all models are equal for coding tasks. The video recommends these as the best options for use with Claude Code in 2026:
| Model | Parameters | RAM Needed | Best For |
|---|---|---|---|
| Qwen2.5-Coder | 7B / 14B | 8GB / 16GB | Best overall — fast, accurate, great at code generation |
| DeepSeek-Coder-V2 | 16B | 16GB+ | Complex reasoning, longer outputs |
| Qwen2.5-Coder:32B | 32B | 32GB+ | Highest quality — needs powerful hardware |
Pull the recommended model:
ollama pull qwen2.5-coder:7bFor better quality on machines with 16GB+ RAM:
ollama pull qwen2.5-coder:14bThe download is 4–9GB depending on the model. Once downloaded, it runs entirely offline.
Step 3 — Connect Claude Code to Ollama
This is the key step — a single command that redirects Claude Code from the Anthropic API to your local Ollama instance:
claude --model ollama:qwen2.5-coder:7bThat is it. Claude Code now uses your local model instead of calling the Anthropic API. No API key needed. No token costs.
What Happens Behind the Scenes
- Claude Code detects the
ollama:prefix in the model name. - Instead of calling
api.anthropic.com, it sends requests tolocalhost:11434(Ollama’s API endpoint). - Ollama runs inference on your local GPU/CPU and returns the response.
- Claude Code receives the response and uses it exactly as it would use an Anthropic API response.
Step 4 — Live Demo: What You Can Do
The video demonstrates three real tasks using Claude Code + Ollama — all free, all local:
1. Generate a FastAPI App from Scratch
claude --model ollama:qwen2.5-coder:7b
> Create a FastAPI app with endpoints for user CRUD operations,
SQLite database, and Pydantic modelsClaude Code generates the project structure, writes the main application file, creates the models, and sets up the database — all from one prompt.
2. Refactor Existing Code
> Refactor the user routes to use dependency injection
and add input validation with proper error handlingIt reads your existing files, understands the patterns, and rewrites them with improved structure — modifying multiple files in a single operation.
3. Fix Bugs
> The /users endpoint returns 500 on duplicate emails.
Fix it and add a proper conflict responseIt locates the relevant code, identifies the issue (missing exception handling), and patches it — including adding the appropriate HTTP 409 response.
Local vs Cloud — Honest Comparison
| Aspect | Claude Code + Ollama (Local) | Claude Code + Anthropic API |
|---|---|---|
| Cost | Free | $0.25–$3 per million tokens |
| Privacy | 100% local — no data leaves your machine | Code sent to Anthropic servers |
| Quality | Very good for common tasks | Best available — especially complex reasoning |
| Speed | Depends on your hardware (GPU helps) | Fast — cloud inference |
| Offline | Yes — works without internet | No — requires internet |
| Setup | Install Ollama + pull model (5 min) | Just set API key (1 min) |
Bottom line: Use local for daily coding, prototyping, and sensitive codebases. Switch to the cloud API when you need the highest quality output for complex architecture decisions or very large codebase reasoning.
Tips for Best Results with Local Models
- Be specific in your prompts. Local models benefit from more context. Instead of “fix the bug,” say “the /users endpoint returns 500 when a duplicate email is submitted — add proper error handling and return HTTP 409.”
- Use the right model size for your hardware. A 7B model running smoothly beats a 14B model that swaps to disk. Check your RAM and choose accordingly.
- Break complex tasks into steps. Instead of “build the entire app,” start with “create the project structure and models,” then “add the CRUD endpoints,” then “add error handling.”
- Keep Ollama running in the background. The model stays loaded in memory, so subsequent prompts are instant. If you close Ollama, it needs to reload the model (10–30 seconds).
- Use a GPU if available. Ollama automatically detects NVIDIA and Apple Silicon GPUs. A GPU makes responses 3–10x faster than CPU-only inference.
Resources
- Ollama Download: ollama.com/download
- Ollama Documentation: github.com/ollama/ollama
- Claude Code Documentation: docs.anthropic.com/en/docs/claude-code
- Ollama Model Library: ollama.com/search
Frequently Asked Questions
Can I really use Claude Code for free?
Yes. By connecting Claude Code to Ollama running a local model like Qwen2.5-Coder, you bypass the Anthropic API entirely. There are no API charges, no token costs, and no monthly fees. Your machine’s compute resources are the only cost. The setup takes under 5 minutes.
Which local model works best with Claude Code?
Qwen2.5-Coder (7B or 14B) and DeepSeek-Coder-V2 are currently the top choices for code generation with Claude Code. Qwen2.5-Coder offers the best balance of speed and quality on consumer hardware. For machines with less RAM, the 7B parameter version runs well. For better quality with 16GB+ RAM, use the 14B variant.
Is Ollama safe and private?
Yes. When you run Ollama locally, all inference happens on your machine. No code, prompts, or data are sent to any external server. This makes it suitable for proprietary codebases and sensitive projects where sending code to cloud APIs is not allowed.
How much RAM do I need to run Ollama with Claude Code?
For the 7B model, 8GB RAM is the minimum. For the 14B model, 16GB RAM is recommended. The model loads entirely into memory during inference, so more RAM allows you to run larger, more capable models without swapping or crashes.
How does the quality compare to the real Claude API?
The Anthropic Claude API (Claude 3.5 Sonnet, Claude 4) produces higher quality output for complex tasks. Local models like Qwen2.5-Coder are excellent for common tasks — generating boilerplate, refactoring, writing tests, and fixing simple bugs. For intricate architecture decisions or very large codebase reasoning, the cloud API has an edge. The tradeoff is cost and privacy versus peak capability.
Video Chapters — Quick Navigation
- 0:00 — Introduction
- 0:50 — The API Cost Problem
- 1:46 — How It Works (Architecture)
- 2:05 — What is Ollama?
- 5:15 — What is Claude Code?
- 10:45 — Key Takeaways
- 11:15 — Subscribe & Resources