Free Tools /AI
RAG Chunk & Embedding Cost Calculator
Plan a knowledge-base chatbot: chunks, embedding cost, vector storage and re-indexing cost from your document volume.
- Free, no signup
- Runs in your browser
- Nothing stored
Calculating…
Who uses it, and for what
Know what it costs to put your help center or contracts behind an AI assistant before you start.
Size the vector database plan and test how chunk size changes the number of vectors.
Budget the monthly cost of keeping the index fresh as documents change.
How this tool works
Converts pages to tokens
Documents × pages × words per page × 1.33 tokens per word.
Splits into overlapping chunks
Each new chunk advances by chunk size minus overlap, so more overlap means more chunks and higher cost.
Prices embeddings and storage
Embedding cost uses your price per million tokens; storage is chunks × dimensions × 4 bytes, before index overhead.
Frequently asked questions
What chunk size should I use?
A common starting point is 300 to 800 tokens with 10 to 20% overlap. Then test retrieval with 20 real questions and adjust.
Is embedding the main cost of RAG?
Usually not. Embedding is cheap; the larger cost is the language model reading the retrieved chunks on every question.
Why does storage matter?
Hosted vector databases price by stored vectors or memory. Higher dimensions and more chunks raise the plan you need.
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