Everyone talks about RAG accuracy. Nobody talks about RAG economics.
A typical RAG query hits 5 cost centers: embedding generation ($0.0001-0.001), vector DB query ($0.0001-0.01), reranking ($0.001-0.01), context assembly ($0.01-0.05), LLM generation ($0.01-0.10).
Total: $0.02-0.17 per query. At 10K queries/day = $6K-51K/month.
Semantic caching reduces LLM calls by 30-60%. Approaches: exact match, semantic cache, prefix cache.
Like this analysis?
Get the weekly engineering economics briefing — one email, every Monday.
Subscribe Free →Most AI features are margin-negative. Here's the exact framework to calculate whether your AI feature makes money.
10 minYour AI feature impresses users but destroys margin. Here's the decision framework.
9 minThe GPU cost decision matrix every AI team needs. On-prem vs. cloud vs. hybrid analysis.
11 minPublished Work
This article expands on ideas from my published work in CIO.com, Built In, Mind the Product, and HackerNoon. View published articles →
The Product Economist — Quantifying engineering economics for technology leaders, PE firms, and boards.