How to Optimize Vector Search When RAM Gets Too Expensive: On-Disk vs. In-Memory ANN Indexes
Machine Learning How to Optimize Vector Search When RAM Gets Too Expensive: On-Disk vs. With the rise of agentic systems, companies are trying to provide as much context to the agents as possible, which requires vector db indexes to grow from an initial million or dozens of millions scale to the hundreds of millions or even billions. At this scale, storing indexes and associated data in RAM will cost thousands of dollars per month, and HNSW can become a scalability bottleneck.
- ▪Machine Learning How to Optimize Vector Search When RAM Gets Too Expensive: On-Disk vs.
- ▪With the rise of agentic systems, companies are trying to provide as much context to the agents as possible, which requires vector db indexes to grow from an initial million or dozens of millions scale to the hundreds of millions or even bi
- ▪At this scale, storing indexes and associated data in RAM will cost thousands of dollars per month, and HNSW can become a scalability bottleneck.
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Machine Learning How to Optimize Vector Search When RAM Gets Too Expensive: On-Disk vs. In-Memory ANN Indexes Architecting cost-effective infrastructure by navigating the latency and storage trade-offs of HNSW, SPANN, and DiskANN Oleg Tereshin Jul 25, 2026 11 min read Share Over the last few years, vector search has become a critical piece of AI infrastructure, powering use cases from RAG and semantic search to agentic memory and context layers. With the rise of agentic systems, companies are trying to provide as much context to the agents as possible, which requires vector db indexes to grow from an initial million or dozens of millions scale to the hundreds of millions or even billions.
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