RAG Is Burning Money — I Built a Cost Control Layer to Fix It

Retrieval-Augmented Generation (RAG) systems often become financially inefficient at scale due to issues like context over-fetching, reprocessing repeated queries, and using expensive models for simple tasks. A cost control layer can be implemented to mitigate these problems by combining semantic caching, intelligent query routing, and budget enforcement. This architecture uses a semantic cache to handle repeat questions, a router to select the most cost-effective LLM for a query's complexity, and a budget layer to prevent overspending. By implementing these components, the system can achieve significant cost reductions of up to 85% without compromising answer quality.