How this calculator works
The Vector Database Storage Cost Calculator estimates the raw storage footprint and monthly hosting cost of the embeddings behind a RAG system. It multiplies your number of vectors (entered in thousands) by the embedding dimensions and the bytes per dimension to get the base vector payload, then applies index overhead to reflect the extra space that approximate-nearest-neighbor structures consume beyond the vectors themselves. That total is converted to gigabytes and multiplied by your $ / GB-month rate, so you can see what a given corpus size actually costs to keep online each month before committing to a provider or model.
The main lever to watch is the dimension-times-bytes product: a 1536-dimension embedding at 4 bytes per dimension costs twice as much to store as a 768-dimension one, and switching from float32 to an 8-bit quantized representation cuts bytes per dimension roughly fourfold. Because cost scales linearly with vector count, growth compounds quickly at scale. The key trade-off is retrieval accuracy versus storage spend — smaller dimensions and quantization lower the bill but can degrade result quality, so test recall at each setting rather than optimizing for storage alone.
Frequently asked questions
How is vector database storage size calculated?
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.
How do I cut vector storage cost?
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.