Automation
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If you don't run your own LLM proxy, how do you know whats going on?
opinion
2 min read
Running a self-hosted LLM proxy can centralize requests to multiple AI providers and models behind an endpoint the operator controls. The setup can log which API key called which model, which client made the request, the request time, and its status. Virtual API keys can limit the spread of a primary provider key across applications, agents, and test harnesses, while a proxy can allow rapid switches between models or providers without reconfiguring every client. The writer argues that consumer-facing AI usage dashboards do not provide the time-series token data needed for engineering decisions. They use a modified self-hosted proxy to add visibility, custom Model Context Protocol server endpoints, and other features. The proxy’s API could also support automated diagnostics; the writer says they can manually identify the cause of a cache miss during a coding session in about 20 seconds. They run this infrastructure at home for learning and at work to maintain control over application visibility, data paths, storage, and inference instrumentation.