RAG Cost Estimator
Estimate the full cost of a RAG (Retrieval-Augmented Generation) pipeline: one-time corpus embedding + monthly vector DB storage + per-query embedding + LLM completion. Compare configurations side-by-side.
Your corpus
Your traffic
Stack
How to use the RAG Cost Estimator
Set your corpus size, query volume, retrieval top-k, and pick an embedding + LLM combo. The estimator splits cost into: one-time corpus embedding (paid once), per-query embedding (small), retrieval (usually negligible — most vector DBs price by storage + reads, not compute), and LLM completion (usually dominant). Vector DB cost is approximated for managed Pinecone.
Costing a full RAG pipeline
A retrieval-augmented generation pipeline spends money in four places: embedding the whole corpus once up front, storing those vectors in a database month after month, embedding each incoming query, and the LLM completion that writes the answer. Looking at any one of those in isolation gives a misleading picture — teams often over-worry about embedding cost when the completion dominates the bill.
This estimator takes your corpus size, query volume, retrieval depth, and a chosen embedding-plus-LLM stack and splits the monthly cost across all four stages, so you can see where the money actually goes. To go deeper on a single stage, use the LLM cost comparator for completion and the vector DB pricing calculator for storage.
Common use cases
- Pipeline budgeting — estimate the full monthly cost of a RAG system before building it.
- Finding the cost driver — see whether embedding, storage, or completion dominates.
- Stack comparison — swap embedding and LLM choices to compare configurations.
- Scaling forecasts — project cost as corpus size or query volume grows.
- Build-vs-buy — weigh a self-hosted embedding model against a paid API.