Microsoft's entire marketing pitch for SQL Server 2025 is 'the AI-ready database.' It went GA on November 18, 2025. We are now four months in. Here is what is actually GA, what is still behind a preview flag, and what that means if you are evaluating this for production.
What Shipped as GA
| Feature | What It Does |
|---|---|
| VECTOR data type | Stores embeddings as float32 arrays |
| VECTOR_DISTANCE | Calculates distance between two vectors (cosine, euclidean, dot product) |
| VECTOR_NORM | Returns the norm (magnitude) of a vector |
| VECTOR_NORMALIZE | Normalizes a vector to unit length |
| CREATE EXTERNAL MODEL | Registers an external AI model endpoint for use in T-SQL |
This is real. You can store vectors, measure distance between them, and register AI model endpoints. These are fully supported in production.
But storing vectors and measuring distance is not AI-powered search. It is the plumbing for it. To actually search vectors at scale, you need what is still in preview.
What Is Still in Preview
All of the following require PREVIEW_FEATURES = ON at the database level:
ALTER DATABASE SCOPED CONFIGURATION SET PREVIEW_FEATURES = ON;
| Feature | What It Does | Why It Matters |
|---|---|---|
| VECTOR_SEARCH | Approximate nearest neighbor search | The function that actually finds similar vectors fast |
| CREATE VECTOR INDEX | DiskANN-powered vector index | Without this, vector search does a full table scan |
| AI_GENERATE_EMBEDDINGS | Generates embeddings from text inside T-SQL | Eliminates external embedding pipelines |
| AI_GENERATE_CHUNKS | Splits text into chunks for embedding | Required for RAG patterns |
| Half-precision vectors | 16-bit vector storage | Cuts storage in half for large embedding sets |
These are the features that make 'AI-ready' actually ready. And they are all behind a preview flag with no announced GA date - that I am aware of.
The Vector Index Problem
The vector index limitations are where this gets particularly rough for anyone thinking about production use. From Microsoft's own documentation:
| Limitation | Impact |
|---|---|
| Table becomes read-only | No INSERT, UPDATE, DELETE, or MERGE while the index exists |
| Single-column integer PK required | Clustered index must be a single integer column |
| No partitioning | Cannot partition the vector index |
| No replication | Vector indexes are not replicated to subscribers |
Read that first row again. A table with a vector index becomes read-only. If you need to add, update, or delete data — ie., if you have an application — you have to drop the index, make your changes, and rebuild it. Azure SQL DB has a workaround for this (ALLOW_STALE_VECTOR_INDEX), but that option is not available in SQL Server 2025.
Erik Darling published a walkthrough today showing these limitations in practice and concluded that vector indexes are 'just not there yet.' I would have to agree.
What You Can Actually Do Today
The GA features are not useless. Here is what works right now without preview flags:
Exact search on small datasets. If you have fewer than ~50,000 vectors (Microsoft's own recommendation), VECTOR_DISTANCE with a TOP(n) ... ORDER BY distance works fine as a brute-force scan. No index needed.
Store embeddings alongside relational data. The VECTOR data type is GA and production-supported. You can generate embeddings externally (Python, Azure OpenAI, whatever), store them in SQL Server, and query them with VECTOR_DISTANCE. That is a real capability.
sp_BlitzCache @AI integration. This uses sp_invoke_external_rest_endpoint to call AI models for query tuning advice — completely separate from the vector features, fully functional, and genuinely useful. I have written extensively about this.
Bottom Line
SQL Server 2025 is 'AI-ready' in the same way a house with plumbing but no fixtures is 'move-in ready.' The foundation is there. The VECTOR data type is real. The distance functions work. But the features that turn it into an actual AI search platform — vector indexes, VECTOR_SEARCH, in-engine embeddings — are all preview, with significant limitations, and no timeline for GA.
That does not mean you should ignore it. It just means that we need to understand exactly where the line is between what is supported in production and what is not. If you are building something today, build on the GA features and keep an eye on the preview features. Do not build production systems on PREVIEW_FEATURES = ON.
Microsoft is shipping the right pieces. They are just not done yet. Or as Erik says, there is still work to be done before they "can be considered mature features in SQL Server".
More to Read
Microsoft: SQL Server 2025 Vector and AI Features Released in RTM
Microsoft: CREATE VECTOR INDEX — Limitations
Microsoft: Vector Search and Vector Indexes in SQL Server
Erik Darling: Vector Indexes Just Not There Yet
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