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

TL;DR

Cost-effective vector search can be achieved by balancing latency and storage through on-disk and in-memory approximate nearest neighbor (ANN) indexing methods like HNSW, SPANN, and DiskANN.

Key Takeaways

• Consider on-disk ANN algorithms for large datasets to reduce RAM costs.
• Use HNSW for low-latency needs with smaller indexes.
• Evaluate trade-offs between retrieval speed and accuracy based on use case.
• Monitor storage costs as your vector index scales to avoid budget overruns.