RAG cost optimization: cut retrieval-augmented LLM spend
RAG concentrates cost in the input: you prepend retrieved context to every query. That makes it one of the most expensive — and most optimizable — LLM patterns.
Cache the parts that repeat
The system prompt and any always-present context (schema, instructions) are identical across queries. Caching that prefix removes it from the per-query bill.
Retrieve less, more precisely
Top-k that's too large is pure waste: you pay input price for chunks the model ignores. Tighter retrieval and reranking cut tokens and often improve answers.
Deduplicate repeated questions
In production, a meaningful share of RAG queries are near-duplicates on the same document. Semantic response caching avoids paying to generate identical answers twice.
Turn this into your number
Drop your usage export into the free analyzer and see how much of this applies to your account — provable savings separated from estimates. Nothing is uploaded.
Analyze my usage — free