TLDR Tech / Data
How to Optimize Vector Search When RAM Gets Too Expensive: On-Disk vs. In-Memory ANN Indexes
Vector search infrastructure is hitting a cost wall at 100M to billion-scale indexes, where RAM-based HNSW becomes expensive and can bottleneck on memory. HNSW delivers the lowest latency for small-to-medium collections, but disk-based ANN options like SPANN and DiskANN cut storage costs by shifting most data to SSD/object storage and optimizing for sequential or minimal disk I/O. For large RAG, semantic search, and agentic memory workloads, the key trade-off is accepting higher, more variable latency in exchange for dramatically lower infrastructure spend.
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