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

Azure Cosmos DB and Microsoft’s DiskANN can help developers achieve an accurate, efficient, and scalable vector search even at massive scale. With DiskANN’s efficient query processing, developers can maximize storage capacity and reduce memory dependency, enabling high-speed similarity searches for all data. DiskANN is integrated into Azure Cosmos DB, thereby allowing developers to build applications that feature built-in vector search, real-time fraud detection, and robust multi-tenancy support. This technology is already powering Microsoft 365, and now it offers developers a means to build modern AI-driven applications. Kirill Gavrylyuk, VP for Azure Cosmos DB, shares how Azure Cosmos DB with DiskANN can deliver an unparalleled speed, efficiency, and accuracy, ideal for modern AI-driven applications.

The tutorial provides quick links to access information on Cosmos DB optimizations with DiskANN, when the DiskANN approach is beneficial, efficient querying, DiskANN compared to HNSW, integrating DiskANN into a new or existing app, real-time transactional AI scenarios, building a fraud detection sample app, vectorizing transactions for anomaly detection, scaling to address high traffic levels, managing multi-tenancy, and the wrap-up. The post also provides links to check out and try the apps.

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