Vector Storage Calculator
Calculate the storage size required for a set of vector embeddings.
Inputs
Storage Size
5.72GB
Storage Size (MB)
5,859MB
Step by step
Raw storage: vectors × dims × bytes/float
1,000,000 × 1536 × 4
= 5.72 GB
How it works
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
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.