Vector Storage Calculator

Calculate vector database storage

A vector database stores embeddings, each a list of numbers representing a piece of text or other data, and its storage requirement grows with the number of vectors, their dimension and the precision of each value. This calculator multiplies these together to estimate the raw storage, and adds an allowance for the index structures that enable fast similarity search. A million embeddings of dimension fifteen hundred and thirty-six in float32 need about six gigabytes of raw storage.

Lower-precision formats such as float16 or int8 roughly halve or quarter the size, at some cost to retrieval accuracy.

Planning a vector store

Estimating storage upfront helps you choose hardware, select a precision, and budget for a retrieval-augmented generation or semantic search system. The embedding dimension, set by the model you use, has a direct effect, so a higher-dimensional model needs proportionally more storage. Quantizing vectors to lower precision is a common way to cut storage and memory, trading a little accuracy for large savings.

The index overhead allowance reflects the extra structures that approximate-nearest-neighbour search builds on top of the raw vectors. All calculation happens locally in your browser.

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