How to Cut LLM Waste with DriftQ

I have been part of teams where we tried to cut LLM costs the obvious ways: using a cheaper model, trimming prompts, capping output tokens, adding caching, maybe routing smaller tasks to a cheaper tier. All of that helps. But a lot of avoidable spend in production isn't really about model pricing. It's workflow waste. Not the kind you notice immediately, either. The sneaky kind: Sometimes fails near the end, so the whole workflow has to return. A flaky provider causes retries that keep redoing the same paid work. A batch job pushes past safe concurrency and starts slamming the endpoint. A "self-healing" agent loop keeps spending in the background until somebody notices. That wasted compute adds up fast. A lot of the time, you are not paying because the model is inherently too expensive. You are paying because your system keeps buying the same work over and over again. That is the layer DriftQ is meant to help with. DriftQ-Core is an open-source Go project that gives you a durable broke