Claude Code with Ollama: Run an AI Coding Agent on Local and Cloud LLMs

#ai #claude #deveopertools

The need
Claude Code is a powerful coding agent in the terminal, but by default it relies on a paid Claude account or API billing. I wanted to use it without depending on a single paid provider: running a model locally to keep my code on my machine, and falling back to free cloud models when a small local model is not enough.

What we will build

Claude Code installed in the terminal.

Ollama installed on Windows.

Claude Code linked to a local LLM through Ollama.

An alternative with Ollama cloud models (limited usage).

Prerequisites

Windows with PowerShell.

Node.js and npm.

Enough disk space for the local model you choose.

1. Install Claude Code

Claude Code needs Node.js. Install it globally with npm and verify the version. (The PowerShell script from the docs failed on my machine, npm worked.)

On first launch, Claude Code asks how you want to log in: a Claude subscription, an Anthropic Console account (API billing), or a third-party platform. In the next steps we skip all of these and use Ollama instead.

2. Install Ollama

Download the Ollama installer for Windows, run it and click Install. Setup extracts the files and installs Ollama under your user folder. When the app opens, a welcome screen explains the idea: run open models with your coding agents. On the sign-up screen, choose “No thanks, I’ll use Ollama locally” so your prompts and code stay on your machine.

3. Link Claude Code with Ollama (local)

Ollama can launch Claude Code already wired to a model, with no manual configuration. Run the command below, pick a model from the list, then give Claude a task.

The list shows cloud models and local models, with the download size for each local one (for example qwen3.5:4b ~6 GB or gemma4:12b ~11 GB). Here I picked the tiny qwen3.5:0.8b. On the first launch, Claude Code asks you to choose a text style for your terminal (you can change it later with /theme).

Claude Code then starts on the local model. It may warn that the model is not recognized and assume a 200k-token context, and the header shows API Usage Billing because Claude Code does not know it is talking to Ollama. Your prompts and code never leave your machine, but speed and quality depend on your hardware and the size of the model.

4. Alternative: use Ollama cloud models (limited usage)

If your machine is too small for a good local model, sign in to Ollama from the terminal when asked; a browser page confirms Device Connected Successfully. The cloud models of the selection list (gemma4:31b-cloud, glm-5.3-flash:cloud, kimi-k3:cloud, deepseek-v4.1-flash:cloud) can then be used from Claude Code. They are far more capable than a tiny local model, but usage is capped.

⚠️ Cloud models run on Ollama’s servers, so your prompts leave your machine. Keep the local option for sensitive code.

Demo

A Java class generated through Claude Code on the Ollama cloud model gemma4:31b-cloud:

Local vs cloud: quick comparison

Local (Ollama): private, no usage cap, but limited by your hardware.
Cloud (Ollama): much more capable models, but usage is limited and prompts leave your machine.

Conclusion

With a single command, Claude Code runs on a local model or on an Ollama cloud model, without a paid Claude subscription. Start local to protect your code, and switch to cloud models when you need more power.

Which do you prefer for your coding agent: local or cloud models? Share your experience in the comments.