Vector Search Optimization: Scaling Embeddings with 80% Cost Reduction
Vector search infrastructure is expensive, but it doesn’t have to be. Ahmad Wael explains how pairing Matryoshka Representation Learning (MRL) with Scalar Quantization can reduce your storage costs by 80%. Learn why float32 is a precision trap and how to optimize your HNSW indices for high-scale production apps.