We’re excited to announce major improvements to DiskANN in SQL Server 2025 RC1, making vector search faster, more scalable, and more storage-efficient than ever before. ⚡ Significantly Faster Index Builds Building DiskANN indexes is now considerably faster, thanks to optimizations that better utilize all available CPU cores and improve the ef...

We’ve just released major updates to our data access libraries, bringing native support for vector data to both the .NET and JDBC ecosystems, unlocking significant performance gains for AI and machine learning workloads. Microsoft.Data.SqlClient 6.1.0 With the new class, you can now handle vector data in binary format, replacing the slower JSON ...

We are happy to announce that Native vector support in Azure SQL Database and Azure SQL Managed Instance is moving to General Availability this summer. Deployments are already underway, and several regions have begun receiving the feature. What is going GA? Integrated Vector Data Type Azure SQL introduces a dedicated VECTOR data type that simpl...

I'm happy to announce that SQL Server 2025 CTP 2.1 is now available, and it brings significant improvements to DiskANN support. While DiskANN remains in preview, each release continues to remove limitations and boost performance. In this release, we’ve made a particularly notable leap in vector index creation speed, with more enhancements on th...

Today, more and more customers are embracing AI with one clear goal in mind: they want to chat with their data. And by "data," they mean all of it:  structured tables, unstructured documents, images, audio, and more. This demand has given rise to hybrid approaches that blend semantic understanding with precise data retrieval. But here’s the chal...

We are thrilled to announce that Azure SQL Managed Instance now supports Vector type and functions in public preview.  This builds on the momentum from Azure SQL Database, where vector support has been in public preview since December. Check out the detailed announcement 💡 Why Choose Azure SQL Managed Instance for AI-Driven Modernization? ...

Storing and querying text embeddings in a database it might seem challenging, but with the right schema design, it's not only possible, it's powerful. Whether you’re building AI-powered search, semantic filtering, or recommendation features, embeddings, and thus vectors, are now a first-class data type. So how do you model them well inside a databa...

OpenAI recently introduced a powerful new feature for developers: structured output using JSON Schema via the parameter. This makes it possible to request responses from a GPT-4o model that strictly match a given schema—no free-text, no guesswork. If you're working with Azure SQL, this is a game-changer. Combined with the stored procedure and SQ...

In today's data-driven world, delivering precise and contextually relevant search results is critical. SQL Server and Azure SQL Database now enable this through Hybrid Search—a technique that combines traditional full-text search with modern vector similarity search. This allows developers to build intelligent, AI-powered search experiences directl...

Vector databases like Qdrant and Milvus are specifically designed to efficiently store, manage, and retrieve embeddings. However, many applications already use relational databases like SQL Server or SQL Azure. In such cases, installing and managing another database can be challenging, especially since these vector databases may not offer all t...