SQL Server 2025 launched this week with native AI capabilities baked directly into the database engine. Not third-party integrations. Not external API calls. Actual AI models running inside the database alongside your queries. Microsoft is betting that the future of data infrastructure isn't just storing and retrieving information—it's understanding it.
What Actually Shipped
SQL Server 2025 includes built-in vector search, real-time analytics, and AI-powered query optimization. But the headline feature is native AI model inference directly in T-SQL queries. You can now call machine learning models as easily as you'd use a traditional database function.
The syntax looks like this: SELECT *, PREDICT(model_name, input_columns) FROM your_table. That's it. No external API calls, no data movement, no complex infrastructure. The model runs inside the database, processes your data, and returns predictions in the same query that retrieves your records.
This works with both Microsoft's pre-trained models and custom models you bring yourself. Import a scikit-learn model, a TensorFlow model, or an ONNX model, register it in SQL Server, and start using it in queries. The database handles model versioning, caching, and performance optimization automatically.
Why This Actually Matters
Traditionally, using machine learning with database data requires: (1) export data from the database, (2) send it to an ML service or application, (3) run inference, (4) write results back to the database. That's slow, expensive, and creates data governance nightmares.
Every time data leaves the database, you multiply security risks, compliance complexity, and performance overhead. If you're doing real-time fraud detection or personalized recommendations, that round-trip latency is unacceptable.
SQL Server 2025 eliminates the round trip. Data never leaves the database. Models run where the data lives. That's faster, more secure, and dramatically simpler from an architecture perspective.
For developers, it means you can add AI to applications without building separate ML infrastructure. If you're already using SQL Server, AI capabilities are now just... there. No new platforms to learn, no additional operational complexity.
The Vector Search Integration
Vector databases have been hyped relentlessly over the past year as essential AI infrastructure. Pinecone, Weaviate, Chroma—dozens of startups building specialized vector stores for similarity search.
Microsoft's bet is that you don't need a separate vector database. Just use SQL Server. The 2025 release includes native vector data types, indexing strategies optimized for high-dimensional embeddings, and query operators for similarity search.
You can store embeddings alongside traditional relational data in the same table. Run vector similarity queries using familiar SQL syntax. Join vector search results with relational queries to combine semantic similarity with structured filters.
A practical example: SELECT * FROM products WHERE VECTOR_DISTANCE(embedding, @query_embedding) < 0.3 AND price < 100 AND in_stock = 1. That's a vector similarity search (finding products semantically similar to a query), combined with traditional filters (price and availability), all in one SQL query.
Real-Time Analytics Without ETL
SQL Server 2025 also eliminates the need for separate analytical databases in many use cases. The engine now supports real-time analytical queries on transactional data without performance degradation.
This is achieved through column store indexes that update in real-time and a query optimizer that automatically routes analytical queries to columnar storage while transactional queries use row storage. From a developer perspective, you just write queries—the database figures out the optimal execution path.
That's huge for companies running both transactional workloads (orders, user accounts, inventory) and analytical workloads (reporting, dashboards, data science) on the same data. Traditionally, you'd need separate OLTP and OLAP databases with ETL pipelines keeping them in sync. Now you can use one database for both.
The Security and Compliance Win
Running AI inference inside the database means data never leaves your security perimeter. For healthcare, financial services, and other regulated industries, that's critical. You can't send patient records or financial transactions to external APIs for ML inference without triggering compliance nightmares.
SQL Server's existing security model—role-based access control, row-level security, encryption at rest and in transit—applies to AI features automatically. If a user can't query certain data, they can't run AI models on that data either. The database enforces permissions consistently.
This is the same advantage that made stored procedures popular decades ago: encapsulating business logic inside the database where it's subject to consistent security and governance policies. AI models are just another form of business logic, and putting them in the database applies the same benefits.
The Performance Question
Running AI inference in-database only makes sense if performance is acceptable. Microsoft claims substantial performance improvements through: model caching (frequently used models stay in memory), query parallelization (inference runs across multiple cores), and result caching (identical predictions are reused).
Early benchmarks from Microsoft show inference latency in the low milliseconds for typical classification and regression models. That's fast enough for real-time applications. Complex deep learning models are slower but still viable for batch processing and offline analytics.
The trade-off is flexibility versus performance. Specialized inference engines like NVIDIA Triton or Amazon SageMaker can achieve lower latency for specific workloads. But they require separate infrastructure, data movement, and operational complexity. For most applications, in-database inference is fast enough and dramatically simpler.
What This Means for the AI Stack
If databases natively support AI inference and vector search, do you still need separate ML platforms, vector databases, and feature stores? For many companies, probably not.
The specialized AI infrastructure startups are built on the premise that traditional databases can't handle AI workloads efficiently. SQL Server 2025 (and competing products from Oracle, PostgreSQL with extensions, and others) challenge that assumption.
This doesn't kill the ML platform market—companies with complex, large-scale AI operations will still need specialized tools. But for the vast majority of businesses that just want to add some AI capabilities to existing applications, native database support is sufficient.
The Open Source Competition
PostgreSQL has had pgvector for a while, adding vector similarity search to the world's most popular open-source database. Supabase and other Postgres-as-a-service providers have made this accessible to developers.
What SQL Server 2025 adds is tighter integration, better performance optimization, and enterprise support. The features aren't unique, but the packaging and ecosystem are.
For companies already invested in Microsoft's stack—Windows Server, Azure, Office 365—SQL Server 2025's AI capabilities are a natural fit. For those preferring open source, PostgreSQL with extensions provides similar functionality without licensing costs.
My Take
This is smart infrastructure design. Put AI where the data lives instead of constantly moving data to where AI lives. That's better for performance, security, and developer ergonomics.
I'm skeptical this replaces specialized AI infrastructure for companies doing cutting-edge machine learning at scale. But for the majority of businesses that just want to add recommendations, predictions, or personalization to their applications, in-database AI is probably sufficient.
The vector search integration is particularly interesting. Vector databases became a hot investment category essentially overnight, with dozens of startups raising millions. If traditional databases absorb that functionality, the standalone vector DB market might evaporate before it fully materializes.
What I'm watching is adoption. Enterprises move slowly on database upgrades, especially for critical systems. SQL Server 2025's AI features sound great, but we won't know if they actually work at scale until companies start using them in production.
If this works as advertised, it represents a fundamental shift in how AI and data infrastructure fit together. Instead of building separate AI platforms, you just... use your database better.