What Is DiskANN? Billion-Scale Vector Search Explained

DiskANN is a graph-based vector search algorithm developed by Microsoft Research, designed to perform efficient approximate nearest neighbor (ANN) searches over large-scale vector datasets using SSDs instead of RAM, thus providing a cost-effective solution for web-scale search. Introduced at NeurIPS 2019, DiskANN utilizes the Vamana algorithm to construct a single-layer directed graph, enabling efficient disk-based traversal, while product quantization (PQ) compresses vectors to reduce in-memory requirements. This technology supports billions of vectors on a single machine with 64GB RAM, achieving high recall and low latency comparable to in-memory solutions, and is particularly advantageous when indexing datasets that exceed 100-200 million vectors or when RAM costs are prohibitive. DiskANN's capabilities are extended by FreshDiskANN, which allows real-time updates without full index rebuilds, making it suitable for dynamic datasets. It is widely implemented in platforms like Couchbase, Azure Cosmos DB, and Microsoft 365, and is ideal for use cases in retrieval-augmented generation (RAG), AI agents, and semantic search, where it reduces infrastructure costs while maintaining performance.

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