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RAG Pipeline Cost Calculator

Calculate the full cost of a RAG pipeline: document embedding, vector database storage, retrieval, and LLM generation. See cost breakdowns per stage and per query.

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Documents→Chunking→Embedding→Vector DB→Retrieval→Generation

Total Chunks

6,000

Total Tokens

3,000,000

Monthly Queries

3,000

Monthly Cost

$73.10

Cost / Query

$0.0244

Monthly Cost Breakdown

Document Embedding
$0.0600
Vector DB
$70.00
Query Embedding
$0.003000
Generation (LLM)
$3.04
StageMonthly Cost% of Total
Document Embedding$0.06000.1%
Vector DB$70.0095.8%
Query Embedding$0.0030000.0%
Generation (LLM)$3.044.2%
Total$73.10100%

Embedding cost assumes re-indexing 1,000 documents monthly. Vector DB cost is a fixed monthly fee. Generation cost is based on 100 queries/day with 5 retrieved chunks of 500 tokens each. Actual costs may vary based on provider billing and volume discounts.

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How to Use RAG Pipeline Cost Calculator

  1. 1

    Configure your documents

    Enter the number of documents, average length, chunk size, and overlap to estimate your embedding volume.

  2. 2

    Select your models

    Choose an embedding model, vector database, and generation model from the dropdowns.

  3. 3

    Set query volume

    Enter how many queries per day your pipeline will handle and the top-K retrieval count.

  4. 4

    Review the breakdown

    See per-stage costs, the visual bar chart, total monthly cost, and cost per query.

Frequently Asked Questions

A RAG pipeline has four main cost stages: (1) embedding your documents into vectors, (2) storing vectors in a database, (3) embedding each user query, and (4) generating answers with an LLM using retrieved context. This calculator estimates all four.

Embedding cost = (total_chunks x chunk_size_tokens / 1,000,000) x price_per_million_tokens. We assume re-indexing once per month. Query embedding cost is calculated separately.

We use approximate monthly costs for managed tiers. Actual costs vary by plan, index size, and query volume. Self-hosted options show $0/month but have infrastructure costs.

The generation model is typically the largest cost driver, since each query sends retrieved chunks (top-K x chunk_size tokens) plus the query itself to the LLM. Reducing chunk size or top-K lowers this cost.