How to Optimize Vector Search When RAM Gets Too Expensive: On-Disk vs. In-Memory ANN Indexes

As AI systems require ever-larger vector databases, storing indexes entirely in memory becomes financially unsustainable. The popular HNSW algorithm provides lightning-fast search but suffers severe performance degradation when moved to disk due to its reliance on random data access. To solve this, on-disk algorithms like SPANN and DiskANN are designed to minimize RAM usage by cleverly organizing data for efficient disk retrieval. This architecture dramatically reduces costs for billion-scale indexes, trading a fraction of search speed for significant financial savings.