RAG System Cost

A RAG system's cost isn't the LLM—it's retrieval quality, data, and evaluation. Here's where the money actually goes, and how to budget for a system that gives right answers.

The demo is cheap; production isn't

Wiring an LLM to a vector database is a weekend demo. Making retrieval accurate enough for production, with evaluation to prove it, is where cost concentrates—because most RAG failures are retrieval failures.

The cost drivers

Driver	Impact
Retrieval quality work	Chunking, embeddings, reranking
Data prep & update frequency	Infrastructure and effort
Evaluation	Proving accuracy
Inference & vector storage	Ongoing cost

The LLM is a small part; retrieval quality and evaluation dominate.

Ongoing cost is real

RAG isn't build-once. Budget for inference (embeddings + generation), re-indexing as data changes, monitoring, and periodic re-evaluation—part of total cost of ownership.

Budget for quality

A cheap RAG build that retrieves the wrong context gives confident wrong answers—worse than none. Spend where it matters: retrieval quality and evaluation. See fine-tuning vs RAG for cost trade-offs.

Why FISTA

FISTA Solutions builds RAG where retrieval is measured and tuned, and is transparent about build and ongoing cost, through AI agents and enablement, backed by a verified 99.9% uptime record.

Budgeting a RAG system that works? Talk to FISTA.