RAG calculator
RAG Cost Calculator
Estimate the one-time and recurring cost of a retrieval-augmented generation system. Model document ingestion, embeddings, vector storage, retrieval context, and LLM output.
Estimate RAG monthly cost
Start with a corpus scenario, replace document and query assumptions, then share or export the cost plan.
- 1Pick corpusChoose the closest document scenario.
- 2Set retrievalEnter documents, chunks, queries, and tokens.
- 3Check monthly costReview embedding, generation, and vector cost.
- 4Share or exportCopy the link or download the CSV.
How the estimate works
RAG cost is split into indexing cost and query-time cost. The core model isembedding cost + vector database cost + LLM input cost + LLM output cost.
- Document words are converted to estimated tokens using a default ratio of 1.33 tokens per word.
- Chunk count increases when overlap is higher, which increases embedding and storage pressure.
- Retrieved chunks become input context on every query, so retrieval depth drives recurring LLM cost.
Estimate ingestion cost
Convert documents into chunks and embedding tokens before building the knowledge base.
Model retrieval context
Account for retrieved chunks that become repeated input context on every answer.
Include storage and refreshes
Add vector database cost and re-indexing frequency for a more realistic RAG budget.
FAQ
What costs are included in a RAG system?
A RAG system can include one-time document embedding, recurring re-indexing, vector database storage, retrieval context tokens, and answer generation tokens.
Why does retrieval depth affect RAG cost?
Every retrieved chunk becomes input context for the language model. More chunks can improve answer coverage, but they also increase input tokens for every query.
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Prices were last checked on 2026-07-04. Use related pages to separate indexing, token, and full product planning.