Guides show how to run Claude Code with local models for zero API cost

Claude Code can now be pointed at local models instead of Anthropic’s hosted API, and in April several guides showed how to do it with Ollama, vLLM, LM Studio, and llama.cpp. The appeal is simple: keep Claude Code’s agentic coding workflow, but run the model on your own machine or server so you are not billed for every request.

The shift matters because Claude Code is not just a chat interface. It is a coding agent that can inspect files, propose edits, and carry out multi-step development tasks, so developers often use it for longer sessions than a normal prompt-and-response tool. When that kind of workflow runs against a paid API, the bill can climb quickly.

Ollama is the most visible piece of the change. According to Ollama’s documentation for Claude Code integrations, the platform now supports Anthropic’s Messages API, which is the interface Claude Code expects when talking to Anthropic models. That compatibility layer means Claude Code can connect to a model served locally through Ollama while still using the same basic client-side workflow.

The technical idea is straightforward. Claude Code sends requests in the format Anthropic’s API uses, and Ollama translates those requests to whichever model is running underneath. In practice, that can be a local model hosted on a workstation, a lab machine, or a private server, depending on the setup.

Other serving stacks are being used too. vLLM is popular for high-throughput model serving on GPU servers, LM Studio is a desktop-oriented tool that makes local model hosting easier for individual developers, and llama.cpp remains a common choice for running quantized models efficiently on limited hardware. The guides that surfaced in April showed that Claude Code is flexible enough to sit on top of each of these back ends when they expose the right API shape.

That flexibility is part of a larger trend in developer tooling. More coding assistants are being built so the user experience stays the same even if the model source changes. For teams, that can mean moving between cloud models and private models without retraining staff on a different interface.

There is still an obvious tradeoff. Local models do not automatically match Anthropic’s best hosted models on reasoning quality, code generation, or instruction following, and the experience will depend heavily on the model you choose and the hardware behind it. But for routine coding work, test scaffolding, refactors, and agent-driven editing sessions, the cost profile changes substantially when the requests never leave your own environment.

Context length is another practical constraint. Agentic coding tools tend to work better when the model can hold a large amount of project context, since they may read multiple files, track prior edits, and keep a running task state. That is why many of the guides focus on models with large context windows rather than smaller chat models that are cheaper to run but easier to overwhelm.

For teams already experimenting with private AI infrastructure, this is a neat fit. The same orchestration patterns used for internal chatbots or self-hosted assistants can now support a coding agent that was originally designed around a hosted frontier model, and the result is a Claude Code session that talks to local infrastructure through an Anthropic-compatible endpoint.