Building AI features usually starts with a cloud API. That is the fastest path when you are experimenting with chat interfaces, summarization, classification, content generation, or agent workflows. You add an SDK, pass an API key, send a prompt, and get a response back. That simplicity is great, but during active development it can also become noisy. Every prompt experiment, failed test, retry, debugging session, and local demo sends another request to a paid service. For one developer, the cost may be small. For a team building AI features every day, those calls can add up quickly. There is also another concern: not every development prompt should leave your machine, especially when you are testing with internal documents, customer-like data, logs, or proprietary examples. Docker Model Runner gives JavaScript developers another option. It lets you run AI models locally using Docker’s workflow and expose them through APIs that feel familiar to developers already using OpenAI-style clients.…