We are excited to publish a new whitepaper titled, Microsoft DiskANN in Azure Cosmos DB, where we examine the impressive capabilities of Microsoft DiskANN, a state-of-the-art indexing system designed for accurate and cost-efficient vector search at any scale.
We start off with a review of key vector search and database concepts, then follow by describing Microsoft DiskANN and how it works. We conduct comparisons against other popular vector indexing algorithms on two datasets and demonstrate how Microsoft DiskANN requires up to 10x lower memory, which is critical to making vector search more cost-effective and scalable. Finally, we discuss how the integration of Microsoft DiskANN with Azure Cosmos DB merges the power of low-latency, accurate vector search with the reliability and scalability of a globally distributed database designed for planet-scale operations. Together, Microsoft DiskANN and Azure Cosmos DB form a database for mission-critical applications providing:
- Cost-effective vector search at any scale: Affordable and accurate search capabilities, regardless of data size and throughput.
- Robustness to incremental changes: Microsoft DiskANN maintains data integrity and high search accuracy during updates, inserts, and deletions.
- Automatic scaling: Ensures seamless performance and cost-efficiency by dynamically adjusting resources to match your application’s workload demands.
- Automated data sharding/partitioning: Optimizes performance and resource utilization.
- High availability: Reduces downtime for continuous service.
- Low-latency transactional operations: Ensures quick data retrieval and modification.
- Built-in multitenancy: Multiple options depending on your requirement for tenant isolation.
- Flexible data modeling: NoSQL schema-free nature enables developers to structure data according to their application needs.
In conclusion, we showcase the remarkable capabilities of Microsoft DiskANN for accurate and cost-efficient vector search at any scale. By integrating DiskANN with Azure Cosmos DB, we combine low-latency, precise vector search with the robust, scalable infrastructure of a globally distributed database. This powerful synergy enables the development of mission-critical applications that are both highly efficient and scalable, meeting the diverse needs of modern AI developers. Read it here.
Learn more about Microsoft DiskANN for Azure Cosmos DB
- Azure Cosmos DB for NoSQL as a Vector Database
- Get started with Azure Cosmos DB AI Samples
- Azure Cosmos DB Vector Search with DiskANN Part 1: Full Space Search
- Microsoft Research talk: Approximate nearest neighbor search systems at scale
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About Azure Cosmos DB
Azure Cosmos DB is a fully managed and serverless NoSQL and vector database for modern app development, including AI applications. With its SLA-backed speed and availability as well as instant dynamic scalability, it is ideal for real-time NoSQL and MongoDB applications that require high performance and distributed computing over massive volumes of NoSQL and vector data.
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