Why Your RAG System Costs 10x More Than You Think

The hidden infrastructure tax of Retrieval-Augmented Generation You've probably heard RAG is the future of LLMs. Retrieval-Augmented Generation lets you ground AI responses in your own data without fine-tuning. It sounds simple. It's not. Most founders and engineers I talk to think RAG costs are straightforward: embed your docs, them in a vector DB, query at inference time. Three steps, done. What they discover in production is brutal: RAG has three separate cost layers that compound aggressively, and the vector database layer - the one nobody thinks about - is the actual stealth killer.