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

# RAG Systems Are Costing Companies Way More Than They Need To If your company uses AI tools to search through documents and provide answers, you're probably spending far more on it than necessary. One engineer discovered that most of these systems are built to give good answers, not to control spending, and created a practical solution that cut costs by 85%—without making the answers worse. The fix combines several smart techniques like reusing previous searches, directing questions to the cheapest processing option, and setting spending limits.
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 s
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