Embedding Quantization Estimator

Estimate the storage vs accuracy trade-off of quantizing embeddings

Embeddings are usually stored as float32 — four bytes per dimension — which gets expensive fast at scale. Quantizing to float16, int8, or binary can cut your vector index by 2× to 32×, but trades away some retrieval quality. This tool shows the exact storage reduction and the typical quality impact from published benchmarks, so you can pick a precision with eyes open. It runs in your browser.

How it works

Raw vector storage is just arithmetic:

bytes_per_vector = dimensions × bytes_per_component
total_bytes      = vectors × bytes_per_vector

where a component costs 4 bytes (float32), 2 (float16), 1 (int8), or 1 bit = 0.125 bytes (binary). The estimator computes this for your inputs and expresses the saving as a multiple of the float32 baseline.

Alongside the numbers it pairs each precision with the typical recall retention reported in the literature:

• float16 — ~2× smaller, essentially lossless (~100% recall).
• int8 — ~4× smaller, typically ~99% recall retained.
• binary — ~32× smaller, typically ~90–96% recall, often recovered to ~98%+ with a full-precision re-ranking pass over the top candidates.

Tips and notes

• Storage shown is raw vectors only — real indexes add overhead for graph links (HNSW), metadata, and IDs; budget headroom on top.
• Binary + rescoring is the sweet spot for large indexes: store binary for the fast first pass, keep a float32 copy (or fetch on demand) to re-rank the top results.
• The quality figures are ranges, not promises — always validate on your own eval set before committing a precision in production.