Production RAG Cost & Latency

Cut the naive $2.50/request pipeline to under $0.30

A startup's RAG system billed at $2.50 per query using GPT-4o for everything. At 1000 daily queries, that was $75K/month. Three changes brought it to $0.28/query: semantic caching (eliminates 35% of LLM calls), query routing (GPT-4o-mini handles 70% of simple queries), and Matryoshka truncation (256 dims saves 70% vector DB costs). $75K/month to $8.4K/month.
— Level 9 · Production RAG Pipeline

+150 XP7 min9 / 10

The Production Cost Stack

A naive RAG pipeline at scale becomes expensive fast. The cost breakdown per query at 1000 QPS:

| Component | Cost Source | Optimization | |-----------|-------------|-------------| | Embedding | API calls or GPU | Matryoshka truncation, self-host at scale | | Vector search | DB compute | HNSW index, pgvector is free | | Re-ranking | API calls | Conditional skip for simple queries | | LLM generation | Tokens in/out | Model routing, caching, shorter contexts |

Semantic caching is the #1 lever: store (query_embedding → response) in Redis. Cache hit = zero LLM cost. Production hit rates: 30-70% depending on query diversity.

Model routing: route simple factual queries to GPT-4o-mini ($0.15/MTok), reserve GPT-4o ($2.50/MTok) for complex reasoning. A classifier adds 5ms and saves 94% on 70% of queries.

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