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

2026-07-25 · Source: Towards Data Science · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure, Robotics & Autonomous Systems · Depth: Advanced, long

Summary

The article discusses optimizing vector search when RAM costs are high, focusing on Approximate Nearest Neighbor (ANN) algorithms, specifically RAM-based (HNSW) versus on-disk (SPANN, DiskANN) indexes. As vector database indexes scale to hundreds of millions or billions for agentic systems, storing them in RAM becomes prohibitively expensive, costing thousands of dollars monthly. For example, a 100 million vector index with 1024 dimensions and float32 precision requires 1.2 TB of storage, costing approximately 6000 USD/month in RAM, compared to about 120 USD/month on remote disk. The article details HNSW's layered graph structure for in-memory speed, contrasting it with SPANN's centroid-based inverted index and DiskANN's Vamana graph, both optimized for disk access. While on-disk solutions offer significant cost savings, they introduce higher latency due to disk I/O, which can range from milliseconds to hundreds of milliseconds, depending on factors like cache state.

Key takeaway

For AI Architects or Machine Learning Engineers designing vector search infrastructure, if your index is projected to exceed hundreds of millions of vectors, you should prioritize on-disk ANN algorithms like SPANN or DiskANN. This choice significantly reduces monthly storage costs from thousands to hundreds of dollars, despite introducing higher query latency. Evaluate your application's latency tolerance, especially for agentic memory versus RAG, to make an informed trade-off between cost and speed.

Key insights

On-disk ANN indexes like SPANN and DiskANN offer significant cost savings over RAM-based HNSW for large vector databases, trading latency for scalability.

Principles

• RAM-based ANN prioritizes search speed.
• On-disk ANN prioritizes cost-efficiency.
• Index scale dictates storage strategy.

In practice

• Use HNSW for small-to-medium vector indexes.
• Consider SPANN or DiskANN for billion-vector scale.

Topics

• Vector Search
• ANN Algorithms
• HNSW
• SPANN
• DiskANN
• Vector Database Costs

Best for: MLOps Engineer, CTO, VP of Engineering/Data, AI Engineer, Machine Learning Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards Data Science.