Milk Infrastructure

Milk Infrastructure starts from a simple observation: every model call your application makes produces a request and a response, and most of that traffic is thrown away. Milk tries to give those calls a second act. Instead of replacing your stack, it inserts itself as a drop-in OpenAI-compatible gateway — you keep the official SDK and your existing code, and change only the base URL and API key.

The system is split into two pieces. Milk Parlor is the gateway: it forwards your calls, returns the answers, and quietly persists eligible request–response pairs to object storage. Milk Man is the agent that works on what comes after. Hand it a goal and it runs commands, spins up cloud models, measures outcomes, and records what it learned, whether that means summarizing accumulated traffic, generating fresh training tasks, or fine-tuning and comparing an open model against a baseline.

Pricing is paid and the project is built around open-source code, with separate repositories for Parlor and Milk Man. The site is refreshingly honest about scope: it states plainly that Milk does not guarantee a better model, that failures are kept alongside successes, and that speed and answer quality are distinct metrics judged against your own chosen baseline rather than a universal leaderboard.