# The Billion-Vector Problem: HNSW vs. DiskANN in Azure AI Search

Page: https://stenobird.com/podcast/m365-fm-modern-work-security-and-productivity-with-microsoft-365-7311214/the-billion-vector-problem-hnsw-vs-diskann-in-azure-ai-search
Text version: https://stenobird.com/podcast/m365-fm-modern-work-security-and-productivity-with-microsoft-365-7311214/the-billion-vector-problem-hnsw-vs-diskann-in-azure-ai-search.md
Podcast: [M365.FM - Modern work, security, and productivity with Microsoft 365](https://stenobird.com/podcast/m365-fm-modern-work-security-and-productivity-with-microsoft-365-7311214)
Published: 2026-06-09T04:00:03+00:00
Episode link: https://www.spreaker.com/episode/the-billion-vector-problem-hnsw-vs-diskann-in-azure-ai-search--72295566
Audio file: https://dts.podtrac.com/redirect.mp3/api.spreaker.com/download/episode/72295566/the_billion_vector_problem_hnsw_vs_diskann_in_azure_ai_search.mp3
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Duration seconds: 4390

## Resource

Most architects default to HNSW because it's the industry standard. It's the algorithm used by most vector databases, the one featured in tutorials, and the option many teams deploy without a second thought.For small and medium-sized workloads, that's often the right decision.But at enterprise scale, a hidden problem begins to emerge.The moment organizations start dealing with hundreds of millions—or even billions—of embeddings, the economics of vector search change dramatically. What looked like a straightforward architectural decision suddenly becomes a conversation about infrastructure budgets, memory consumption, scalability, and long-term sustainability.In this episode of the M365 FM Podcast, we explore one of the most important design decisions facing enterprise AI architects today: when should you use HNSW, and when does DiskANN become the better option?More importantly, we examine how this decision impacts Azure AI Search, Azure Cosmos DB, Microsoft 365 Copilot-style architectures, Retrieval-Augmented Generation (RAG) systems, and the future of large-scale enterprise search. WHY VECTOR SEARCH CHANGES EVERYTHING Traditional search systems rely on keywords. They look for exact matches between a query and the words stored inside documents. While this approach works reasonably well for structured content, it struggles when users describe concepts differently than the documents themselves.Vector search solves this challenge by converting both documents and queries into embeddings—high-dimensional numerical representations of meaning. Instead of searching for matching words, vector databases search for semantic similarity.This is the foundation of modern AI-powered search experiences, enterprise copilots, and Retrieval-Augmented Generation systems. It allows users to…

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