Developer Uses Lightweight Sidecar to Centralize AI Model Endpoint Config in CI

A developer faced repeated CI failures after free AI model routes changed, because the same endpoint URL was hardcoded across multiple GitLab jobs, scripts, and notebooks. To fix this, they built a minimal Python sidecar service that acts as a single source of truth for model configuration, returning the current base URL, model name, and timeout via a simple HTTP endpoint. CI jobs now query the sidecar before making any model calls, meaning a route change requires updating only the sidecar's environment variables rather than editing multiple files. A contract test validates the sidecar's response shape before any real model call is attempted, catching misconfiguration early and avoiding wasted API tokens. The author, writing as part of MonkeyCode's product outreach, acknowledges limitations such as a single point of failure if the sidecar goes down and the inability to detect drops in model output quality.