Vector Search using 95% Less Compute | DiskANN with Azure Cosmos DB

Microsoft Mechanics Podcast

Ensure high-accuracy, efficient vector search at massive scale with Azure Cosmos DB. Leveraging Microsoft’s DiskANN, more IO traffic moves to disk to maximize storage capacity and enable high-speed similarity searches across all data, reducing memory dependency. This technology, powering global services like Microsoft 365, is now integrated into Azure Cosmos DB, enabling developers to build scalable, high-performance applications with built-in vector search, real-time fraud detection, and robust multi-tenancy support.

Join Kirill Gavrylyuk, VP for Azure Cosmos DB, as he shares how Azure Cosmos DB with DiskANN offers unparalleled speed, efficiency, and accuracy, making it the ideal solution for modern AI-driven applications.

► QUICK LINKS: 00:00 - Latest Cosmos DB optimizations with DiskANN 02:09 - Where DiskANN approach is beneficial 04:07 - Efficient querying 06:02 - DiskANN compared to HNSW 07:41 - Integrate DiskANN into a new or existing app 08:39 - Real-time transactional AI scenario 09:29 - Building a fraud detection sample app 10:59 - Vectorize transactions for anomaly detection 12:49 - Scaling to address high levels of traffic 14:05 - Manage multi-tenancy 15:35 - Wrap up

► Link References

Check out https://aka.ms/DiskANNCosmosDB

Try out apps at https://aka.ms/DiskANNCosmosDBSamples

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