#460 · AI Cost Tool

RAG Storage Calculator

Estimate RAG storage, chunk count, vector memory, monthly storage cost, and retrieval cost for vector databases.

Calculator

RAG storage inputs
docs
tokens
tokens
dims
bytes
$
queries
$
Pricing reference date: 2026-06-19. Default rates are editable estimates. Verify current provider pricing before final budgeting.
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Growth forecast

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Cost Breakdown

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How to use this calculator

  1. Enter document count and average tokens.
  2. Set chunk size and embedding dimension.
  3. Add metadata size per chunk.
  4. Enter storage and retrieval prices.
  5. Review vector count, storage size, and monthly operating cost.

What the result means

RAG storage cost depends on how many chunks you create and how large each vector plus metadata record is.

Chunks = documents × ceil(tokens per document ÷ chunk size). Storage GB ≈ chunks × (dimension × 4 bytes + metadata bytes) ÷ 1GB.

Actual vector database billing may include read units, write units, minimum commitments, backups, replicas, or regional pricing.

Example calculation

100,000 documents with 800 tokens each and 400-token chunks create about 200,000 vectors before metadata and indexing overhead.

Tips for better results

  • Avoid overly small chunks.
  • Keep metadata useful but compact.
  • Track retrieval queries separately.
  • Compare storage, read, and write charges.

FAQ

What is RAG storage?

RAG storage is the vector database capacity needed to store embedded chunks, metadata, and retrieval indexes for retrieval augmented generation.

How is RAG storage calculated?

The calculator combines your usage assumptions with editable prices, fixed costs, and volume assumptions to estimate cost, savings, or capacity.

Is this estimate accurate?

It is a planning estimate. Actual bills can differ because providers change prices, apply tiers, add taxes, or bill extra features separately.

What affects the result most?

The largest drivers are usage volume, output length, tool calls, review work, fixed platform costs, and the price per unit you enter.

How can I improve the result?

Reduce unnecessary tokens, batch low-priority work, use smaller models where possible, cache repeated context, and review provider pricing regularly.

What are common mistakes?

Common mistakes include ignoring retries, forgetting fixed monthly costs, using outdated token prices, and assuming every task needs the most expensive model.

When should I use this calculator?

Use it before launching, scaling, or changing an AI workflow so you can estimate budget impact before real usage grows.

Sensitivity analysis

ScenarioEstimate
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+25% usage—
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