RAG Pipeline Cost Calculator
Estimate the monthly cost of a Retrieval-Augmented Generation (RAG) pipeline including indexing, embeddings, and LLM inference.
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How to use this calculator
- 1
Enter the total number of documents in your knowledge base and their average token length.
- 2
Set your expected monthly query volume and how many chunks are retrieved per query.
- 3
Select the embedding model and LLM you plan to use.
- 4
Review the one-time indexing cost and recurring monthly costs.
Frequently asked questions
What is RAG?
Retrieval-Augmented Generation (RAG) is a technique where you first retrieve relevant text chunks from a vector database based on a user query, then pass those chunks to an LLM along with the query to generate a grounded, accurate response. It combines the breadth of LLMs with up-to-date domain knowledge.
What does "document refresh" mean?
Documents change over time. This calculator assumes 10% of your knowledge base is updated or re-indexed each month. If your data is static, the monthly embedding refresh cost will be near zero.
How do I reduce RAG costs?
Use a smaller, cheaper embedding model (text-embedding-3-small vs ada-002). Cache query embeddings for popular or repeated questions. Reduce the number of chunks retrieved per query (from 5 to 3 if quality holds). Route simple queries to Claude Haiku or GPT-4o-mini instead of more expensive models.
RAG Pipeline Cost Calculator — Embeddings + LLM Inference
Understanding RAG cost components
A RAG pipeline has three cost layers: one-time document indexing (embedding every chunk), ongoing re-indexing as documents update, and per-query LLM inference. For most production applications, LLM inference dominates monthly spend while indexing is a comparatively small one-time cost.
Optimizing chunk size for cost and quality
Larger chunks cost more to embed and consume more LLM input tokens, but they provide more context per retrieval. Smaller chunks are cheaper and more precise but may miss surrounding context. Most practitioners find 200–500 token chunks with 5–10% overlap to be an effective starting point. Experiment with your specific corpus to find the sweet spot.
Learn more from an authoritative source:
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Results are estimates for informational purposes only and do not constitute professional financial, medical, legal, or technical advice. Read full disclaimer →