Vector indexing enables semantic search over diverse corpora and has become an important interface to databases for both users and AI agents. Efficient vector search requires deep optimizations in database systems. This has motivated a new class of specialized vector databases that optimize for vector search quality and cost. Instead, we argue that a scalable, high-performance, and cost-efficient vector search system can be built inside a cloud-native operational database like Azure Cosmos DB while leveraging the benefits of a distributed database such as high availability, durability, and scale. We do this by deeply integrating DiskANN, a state-of-the-art vector indexing library, inside Azure Cosmos DB NoSQL. This system uses a single vector index per partition stored in existing index trees, and kept in sync with underlying data. It supports < 20ms query latency over an index spanning 10 million vectors, has stable recall over updates, and offers approximately 43× and 12× lower query cost compared to Pinecone and Zilliz serverless enterprise products. It also scales out to billions of vectors via automatic partitioning. This convergent design presents a point in favor of integrating vector indices into operational databases in the context of recent debates on specialized vector databases, and offers a template for vector indexing in other databases.

Azure SQL Database and the upcoming release of SQL Server enhance Online Index Rebuild to provide fault-tolerance and allow index rebuild operations to resume after a system failure or a user-initiated pause. SQL Server is the first commercial DBMS to support pause and resume functionality for index rebuilds. This is achieved by splitting the operation into incremental units of work and persisting the required state so that it can be resumed later with minimal loss of progress. At the same time, the proposed technology minimizes the log space required for the operation to succeed, making it possible to rebuild large indexes using only a small, constant amount of log space. These capabilities are critical to guarantee the reliability of these operations in an environment where a) the database sizes are increasing at a much faster pace compared to the available hardware, b) system failures are frequent in Cloud architectures using commodity hardware, c) software upgrades and other maintenance tasks are automatically handled by the Cloud platforms, introducing further unexpected failures for the users and d) most modern applications need to be available 24/7 and have very tight maintenance windows. This paper describes the design of "Resumable Online Index Rebuild" and discusses how this technology can be extended to cover more schema management operations in the future.