Cut vector-DB memory cost up to 24x with binary quantization plus Matryoshka embeddings, with single-digit recall loss

Towards Data Science·medium signal

Binary quantization now drops a vector's RAM footprint to ~1/24 of full precision (scalar quantization to ~1/3.75), and stacking it with Matryoshka Representation Learning yields ~80% cost reduction while keeping recall loss in the single digits for most engines. Builders on pgvector should note pgvectorscale's StreamingDiskANN with statistical binary quantization serves disk-resident corpora far larger than RAM — the cheap path to scaling RAG past the memory wall.