Vector Storage Calculator
Calculate the storage size required for a set of vector embeddings.
About Vector Storage Calculator
Vector storage size is simply the number of vectors multiplied by dimensions and bytes per element. One million 1536-dimensional FP32 vectors requires about 5.7 GB of raw storage. Actual database size includes index overhead (typically 20-100% extra).
Formula
Vector Storage
size = num_vectors × dimensions × bytes_per_float
- num_vectors
- Total number of embedding vectors
Worked example
With the default values below, Vector Storage Calculator returns Storage Size of 5.72. Change any input above and every figure here is recalculated in your browser.
Inputs used
- Number of Vectorsminimum 1
- 1,000,000
- Dimensionsminimum 1
- 1,536
- Bytes per Floataccepts 1–8
- 4
Results
- Storage Size
- 5.72 GB
- Storage Size (MB)
- 5,859 MB
Step by step
- 1Raw storage: vectors × dims × bytes/float= 5.72 GB
Assumptions and limits
- The result is derived only from the 3 inputs listed above. Anything not among them is not part of the calculation.
- In particular, this calculator does not take inflation, tax, fees or charges or compounding frequency into account.
- Figures are rounded for display to 2 decimal places, so totals built from several rounded parts can differ slightly from the unrounded arithmetic.
How these calculators are built and tested is described on the Methodology page. If a figure looks wrong, please report it.
Frequently Asked Questions
Does the vector database add overhead?
Yes — ANN indexes (HNSW, IVF) typically add 20-100% overhead on top of raw vector storage for graph structures, quantization codebooks, and metadata.
Can I reduce storage with quantization?
Yes — product quantization (PQ) or scalar quantization can reduce storage by 4-8× with minimal recall loss. Binary quantization gives 32× reduction but significantly impacts accuracy.