What your embeddings actually cost to store — vectors, dimensions and index overhead.
Vector storage scales with embedding size times chunk count, plus index overhead — so smaller models and quantisation pay off at scale. Add the one-off cost to embed the corpus on the embeddings cost calculator, and the query-time spend on the RAG cost calculator.
Free vector-database storage calculator — turn a vector count, embedding dimensions and index overhead into the real storage size and monthly cost of a RAG…
Each vector is its dimension count times bytes per number — a 1536-dimension float32 vector is about 6 KB. Multiply by how many vectors you store, then add the index overhead (roughly 20–50% for HNSW-style graphs). That total, divided by a billion, is your gigabytes, which the provider bills per GB-month.
Use a smaller-dimension embedding model, quantise vectors from float32 to int8 or binary (a 4× or larger saving), and chunk documents less aggressively so you store fewer vectors. Dropping unused metadata and pruning stale vectors also helps once a RAG corpus grows into the millions.
Cost at different volumes:
| Volume | Monthly | Yearly |
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