Why HNSW Isn't the Universal Solution for Vector Databases: The Rise of IVF

HNSW, while popular for its speed and accuracy in vector similarity search, faces limitations in large-scale applications due to its memory-intensive nature. This article argues that disk-based alternatives like IVF (Inverted File Index), especially when combined with quantization techniques (RaBitQ, PQ, SQ, ScaNN), offer superior speed and scalability for massive datasets. IVF, by quantizing and compressing vectors, reduces memory footprint and leverages efficient prefetching and sequential scans for significantly faster search. Insertion and deletion costs are also lower. While HNSW excels in smaller-scale applications, IVF with quantization emerges as the more advantageous choice for massive datasets.