What a RAG pipeline costs per month

Open formula. Browser-based calculations. Source-dated rates. No API keys. No provider login. No tracing SDK. No account required. Input payloads are not stored.
Pricing source date: 2026-05-28 · Build verification: 2026-05-28 · Stale threshold: 30 days. Estimates combine pricing assumptions across the RAG stack: embedding generation, vector-store usage, retrieved-context size, and generation-model tokens. Each component can price differently, so verify embedding, vector database, and generator-model rates before committing to a production RAG design.

Worked example

Example inputs: 5,000 queries/day over a 10M-token corpus, OpenAI text-embedding-3-small, Pinecone vector store

Pricing last updated 2026-05-28 — verify with provider before committing to commitments or contracts. RAG pipeline: $2098.62/mo at 150,000 queries. Breakdown: embed $0.34, vector store $1.28, generation $2097.00. Cost/query: $0.0140. Dominant component: generation. Generation typically dominates RAG cost — a cheaper generator model is the lever with largest impact.

Example inputs — your own numbers will differ. This is a computed planning estimate, not a quote.

Diagnostic output: Each calculation returns Cost classification, Dominant cost driver, Decision threshold, and Sensitivity. Focus for this tool: Dominant RAG cost layer and component-substitution threshold.

About this calculator

Enter query volume, corpus size, chunking, embedding model, vector store, and generator; Model Ruler combines embedding, storage, retrieval context, generation, and reindexing into one monthly estimate and names the dominant layer. Verify each component price before production rollout.

Methodology summary

Combines embedding, vector-store, retrieved-context, generation, and reindexing assumptions into one monthly RAG cost estimate.

Formula: monthly_rag_cost = monthly_embedding_cost + monthly_vector_store_cost + monthly_generation_cost

Audit coverage: RAG pipeline layers · Context growth

Open the full methodology for this calculator.

Frequently asked

What does this calculator compute?
It combines embedding, vector-store, retrieved-context, generation, and reindexing costs into a single monthly RAG estimate and identifies the dominant cost layer.

What should I check before committing to a workload shape?
Cost splits across three stages — embedding generation, vector-store queries, and the generation model. One stage usually dominates; check whether re-embedding frequency, retrieved-chunk count, or generation token volume is the real driver before optimizing the others.

Is this a quote or a benchmark?
Neither — it is a computed estimate, not a vendor quote. The worked example above shows the numbers this calculator produces for a representative rag pipeline cost workload; your own inputs move the result.

Related tools

Estimates combine pricing assumptions across the RAG stack: embedding generation, vector-store usage, retrieved-context size, and generation-model tokens. Each component can price differently, so verify embedding, vector database, and generator-model rates before committing to a production RAG design. View methodology.