Embeddings cost & vector storage estimator
How much will it cost to embed your docs and store the vectors? Plug in corpus size, chunk settings, and model — get initial + monthly embedding cost and storage on typical vector DBs.
Corpus
Chunking
Embedding model
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Re-embed frequency
Numeric inputs sync to the URL. Assumes full corpus re-embedded each period.
Estimate
Storage = vectors × dimensions × 4 bytes. Indexes and metadata add overhead not modeled here.
Typical vector store costs
Static rough numbers for storage only — query/read pricing not included.
Everything runs in your browser. Prices are a mid-2026 snapshot — check vendor pages before you budget.
About this tool
RAG has two bills: embedding API calls and storing/querying vectors. This estimator counts chunks from document size, chunk length, and overlap, then multiplies by embedding price and vector dimensions.
Real pipelines add metadata, hybrid search indexes, and incremental updates. Use this for order-of-magnitude planning — then prototype with a few hundred docs and read your actual dashboard.