RAG Is Burning Money — I Built a Cost Control Layer to Fix It
Read the original on Towards Data Science →Most RAG systems are optimized for answer quality, not cost—and that blind spot gets expensive fast. In this article, I break down a production-ready cost control layer combining semantic caching, query routing, token budgeting, and circuit breaking, achieving an 85% reduction in LLM costs without sacrificing answer quality.
Summary generated by The Flow from the publisher's feed. The full article lives at Towards Data Science.