How to Optimize Vector Search When RAM Gets Too Expensive: On-Disk vs. In-Memory ANN Indexes
Machine LearningArchitecting cost-effective infrastructure by navigating the latency and storage trade-offs of HNSW, SPANN, and DiskANN
Oleg Tereshin
Scaling Vector Search: Comparing Quantization and Matryoshka Embeddings for 80% Cost Reduction
Machine LearningNavigating the performance cliff: How pairing MRL with int8 and binary quantization balances infrastructure costs with retrieval accuracy.
An analysis of how flattening structured data can boost precision and recall by up to 20%