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

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

► Unfamiliar with Microsoft Mechanics?
As Microsoft's official video series for IT, you can watch and share valuable content and demos of current and upcoming tech from the people who build it at Microsoft.

► Keep getting this insider knowledge, join us on social:

#CosmosDB #DiskANN #VectorSearch #Azure