Vector Search in Azure Databases

Paul Hernandez describes the current state of production-ready vector search options in Azure:

Vector databases and vector search are becoming increasingly important in modern applications due to their ability to handle complex and high-dimensional data efficiently. In today’s data-driven world, applications such as recommendation systems, image and video retrieval, natural language processing, and anomaly detection rely heavily on the ability to search and analyze large volumes of data quickly and accurately. Vector databases store data in the form of vectors, which allows for more sophisticated and nuanced searches compared to traditional databases. Vector search techniques enable these applications to find similar items, detect patterns, and make predictions by comparing the distances between vectors. This capability is crucial for delivering personalized user experiences, improving search accuracy, and enhancing overall application performance. As a result, vector databases and vector search are essential components in the toolkit of modern data scientists and engineers.

In this article, we will discuss several Azure services that support vector search, including Azure Database for PostgreSQL Flexible Server, Azure Cosmos DB, and Azure Cognitive Search. Each of these services offers unique features and capabilities that make them suitable for implementing vector search in various applications.

Click through for details, as well as links to more resources. Paul didn’t include Azure SQL Database’s vector capabilities, though that’s in preview right now and I’m not actually sure how well it will perform compared to these other options.