Summary
Microsoft SQL Server 2025 offers AI capabilities that are directly integrated into T-SQL, enabling organizations to leverage their existing investments in SQL Server infrastructure, skills, and processes.
This blog initially appeared on Anthony Nocentino’s blog. It has been republished with the writer’s credit and consent.
Today, November 18, 2025, marks the official general availability of Microsoft SQL Server 2025. Organizations can now deploy production-grade SQL Server 2025 and take immediate advantage of its enterprise-ready AI features, advanced security, and modern developer capabilities, all tightly integrated with the Microsoft data platform. This release unifies AI, analytics, and operational excellence in a single, GA-supported version—making next-gen artificial intelligence practical for everyday workloads without requiring a learning curve or radical architectural changes.
With the general availability of SQL Server 2025, Microsoft is positioning its flagship database platform as the premier enterprise-ready AI and analytics solution. Having experienced both preview and now GA builds, it’s clear this release represents a significant leap for organizations to unlock modern AI capabilities using their existing SQL Server investments.
Alongside native AI capabilities, it introduces JSON indexing, GraphQL support via Data API Builder, and zero-ETL Fabric mirroring for seamless integration with Microsoft Fabric. These enhancements expand SQL Server’s reach—from traditional OLTP to AI-powered, hybrid, and developer-centric workloads—without increasing architectural complexity.
While many cloud-native and niche vector databases have emerged to support AI workloads, SQL Server 2025 brings these capabilities to your existing operational data platform. This integration offers unique advantages that standalone AI solutions can’t match without significant integration efforts and security considerations.
What Are The Latest Updates on SQL Server 2025?
Microsoft has officially released SQL Server 2025 for general availability, introducing advanced enterprise AI features including Copilot integration in SQL Server Management Studio (SSMS), expanded vector search support, half-precision float data types for embeddings, and full support for production workloads. These include Copilot integration in SQL Server Management Studio (SSMS), expanded vector search support with improved performance, and half-precision float data types for embeddings—key for accelerating AI/ML and Retrieval-Augmented Generation (RAG) scenarios directly within SQL Server. These updates further reduce reliance on external vector databases and simplify deployment of AI features within the data platform.
T-SQL Programming Language: Familiar Skills for New AI Challenges
The most compelling aspect of SQL Server 2025’s AI capabilities is their integration directly into T-SQL. This isn’t a bolt-on solution but a thoughtful expansion of what you can do with the language developers and DBA teams already know.
Key here is that teams don’t need to learn a new programming language or platform. The same T-SQL skills they’ve used for years now extend to AI operations. AI functionality can be incorporated into stored procedures, functions, and applications with minimal changes using skills, tools, and techniques familiar to developers and DBAs.
In my experience working with clients implementing AI-based solutions, the challenge is integration with existing operational data. SQL Server 2025 eliminates that gap. Your AI features run right where your data already lives, leveraging existing security and availability features, simplifying architectures, and accelerating time to value.
RC1 also now provides the aforementioned Copilot integration directly in SSMS, providing AI-assisted code completion, performance insights, and troubleshooting recommendations right inside the environment most DBAs already use daily. This integration reduces the learning curve for teams exploring AI-driven development within SQL Server and brings the familiar “Copilot everywhere” experience into the database realm.
Let’s examine Microsoft’s approach to integrating AI into SQL Server 2025 using a code sample they provided as part of the demos in the public preview. The code below shows an AI-enabled semantic search implementation, highlighting the platform’s new vector capabilities.
It follows a four-step process:
- Define an external model connection. Here, I’m using an Ollama embedding generation REST endpoint. You’re free to use your model of choice.
- Add a VECTOR(768) column for storing embeddings to a table in your database, here it’s Product. We are also adding a column chunk. This is the text representation of the values used to generate the embedding.
- Generate the embeddings for Products by combining product details texts into a chunk of text and passing that into the new AI_GENERATE_EMBEDDINGS function to generate the embeddings by passing them to our external model ollama, then storing the generated embedding in our newly created embeddings column.
- Perform a natural language vector similarity search in which a conversational query about a red bike that doesn’t cost much is converted to a vector embedding using AI_GENERATE_EMBEDDINGS and compared against product embeddings stored in our table using cosine distance using the function vector_distance.
— Step 1: Create an External Model used for embedding generation ———————-
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EATE EXTERNAL MODEL ollama WITH ( LOCATION = ‘https://model-web:443/api/embed’, API_FORMAT = ‘Ollama’, MODEL_TYPE = EMBEDDINGS, MODEL = ‘nomic–embed–text’ |
— Step 2: Altering a Table to Add Vector Embeddings Column —————————-
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E [AdventureWorks2025] GO ALTER TABLE [SalesLT].[Product] ADD embeddings VECTOR(768), chunk NVARCHAR(2000); |
— Step 3: CREATE THE EMBEDDINGS (This demo is based off the MS SQL 2025 demo repository)
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DATE p SET [chunk] = p.Name + ‘ ‘ + ISNULL(p.Color, ‘No Color’) + ‘ ‘ + c.Name + ‘ ‘ + m.Name + ‘ ‘ + ISNULL(d.Description, ”), [embeddings] = AI_GENERATE_EMBEDDINGS(p.Name + ‘ ‘ + ISNULL(p.Color, ‘No Color’) + ‘ ‘ + c.Name + ‘ ‘ + m.Name + ‘ ‘ + ISNULL(d.Description, ”) USE MODEL ollama) FROM [SalesLT].[Product] p JOIN [SalesLT].[ProductCategory] c ON p.ProductCategoryID = c.ProductCategoryID JOIN [SalesLT].[ProductModel] m ON p.ProductModelID = m.ProductModelID LEFT JOIN [SalesLT].[vProductAndDescription] d ON p.ProductID = d.ProductID AND d.Culture = ‘en’ WHERE p.embeddings IS NULL; |
— Step 4: Performing a vector similarity search —————————————-
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CLARE @search_text NVARCHAR(MAX) = ‘I am looking for a red bike and I dont want to spend a lot’; DECLARE @search_vector VECTOR(768) = AI_GENERATE_EMBEDDINGS(@search_text USE MODEL ollama); SELECT TOP(4) p.ProductID, p.Name, p.chunk, vector_distance(‘cosine’, @search_vector, p.embeddings) AS distanc FROM [SalesLT].[Product] p ORDER BY distance; |
This example shows how SQL Server 2025 integrates AI directly into the database using familiar T-SQL syntax. This allows developers to implement semantic search without learning new platforms or languages. You keep the AI processing close to where the data already resides, keeping it secure, protected, and highly available.
Get your hands on this code now and head over to my GitHub repo Ollama SQL FastStart for a full-stack solution showcasing SQL Server 2025 and vector, all implemented in Docker to get your development stack up and running in a single line of code.
Enterprise Grade Availability and Disaster Recovery
One of the challenges with many purpose-built vector databases is their relative immaturity in critical enterprise requirements like high availability (HA) and disaster recovery (DR). SQL Server, on the other hand, brings a long history of hardened capabilities to support your database platform:
- Backup and restore: The same familiar backup and restore operations work with vector data, allowing you to leverage your existing backup strategies, including features like T-SQL Snapshot Backup.
- AlwaysOn availability groups: Your AI-enabled databases can participate in availability groups, providing high availability and disaster recovery for your AI workloads. This is crucial for mission-critical AI applications that can’t afford downtime.
- Failover cluster instances (FCIs): For those preferring the shared storage model, FCIs continue to provide an excellent HA solution compatible with vector data.
This is particularly important as AI becomes more central to business operations. When your recommendation engine or document processing systems are directly tied to revenue or customer experiences, having mature HA/DR capabilities becomes core to any system architecture.
Enterprise Grade Security
Security remains one of the most critical considerations when implementing AI systems. SQL Server 2025 brings its comprehensive security model to AI workloads:
- Row-level security (RLS): Apply fine-grained access control to your AI data, ensuring users only see what they’re authorized to access. This is vital for data-driven applications or when dealing with sensitive information, controlling who can access exactly what information in a database, row store, or vector store.
- Multi-layered encryption: From TDE for data at rest to Always Encrypted for sensitive columns to TLS encrypted connections, SQL Server provides comprehensive encryption capabilities that extend to vector data.
- Active Directory/Entra ID integration: Leverage your organization’s existing identity management for authentication and authorization, making security management consistent with your other enterprise applications.
When we consider that AI often involves sensitive data, these security capabilities become even more critical. SQL Server’s mature security model protects embeddings and other AI assets at the same level as your traditional structured data.
Modern Data and Developer Features
Beyond AI, SQL Server 2025 brings a wave of developer-friendly enhancements that align with modern app architectures:
- JSON enhancements: A new JSON type, native indexes, and expanded T-SQL functions simplify working with semi-structured data.
- GraphQL support: Built-in Data API Builder (DAB) enables low-code REST and GraphQL endpoints for rapid application development.
- Change feeds from the transaction log: Enable near real-time sync to external systems like Azure Event Hub and Kafka—critical for streaming analytics and microservices architectures.
- Fabric mirroring: Delivers zero-ETL data sharing with Microsoft Fabric, allowing real-time analytics without impacting transactional workloads.
- Native AI Integration: Vector data type for semantic search and retrieval-augmented generation (RAG) workloads; tight integration with Azure OpenAI and Copilot in SSMS.
- Modern Data Types: JSON enhancements (native JSON data type, indexing), improved graph querying, and built-in low-code GraphQL endpoints.
- Real-time Operational Analytics: Change feeds from the transaction log for streaming, with zero-ETL integration for Microsoft Fabric.
- Security and Availability Upgrades: Enhanced Always On availability groups, enterprise grade HA/DR, row-level security and multi-layered encryption extended to vector data, and deeper Active Directory and Entra ID integration.
- Performance Improvements: Faster batch mode processing, improved locking, and reduced query contention supporting both transactional and AI workloads.
Together, these features close the gap between traditional relational systems and modern app-data stacks, empowering teams to build data-rich, AI-enabled applications faster.
DBA Operational Skill Sets
Perhaps the most overlooked advantage of using SQL Server for enterprise AI is the ability to leverage existing DBA skills and support systems:
- Familiar monitoring and management tools: The same tools DBAs use to manage traditional SQL Server workloads apply to AI-enabled databases, from Query Store to Extended Events and mature third-party monitoring solutions, like Redgate’s SQL Monitor.
- Enterprise support: Microsoft’s support infrastructure and a vast ecosystem of partners and consultants means you have reliable assistance when issues arise.
- Deep community: The SQL Server community is one of the largest and most active in the database space, with vast resources for troubleshooting, optimization, and knowledge sharing.
Wrapping Things Up
Customers can now deploy SQL Server 2025 directly in production environments with the full support of Microsoft and its partner ecosystem. All features, including integrated AI operations, vector storage, and advanced developer tools, are production-ready and backed by the release’s mature reliability and security enhancements.
SQL Server 2025 introduces AI capabilities to traditional enterprise data platforms (I can’t believe I just said traditional databases, but here we are). Organizations can implement AI solutions without the complexities of integrating separate systems, transferring data, or retraining their teams. Customers can bring AI into their enterprise utilizing the existing T-SQL skills of developers and DBAs, along with proven availability and disaster recovery mechanisms, enterprise-grade security, and established operational practices without increasing the technical complexity of their platforms.
While many vendors push organizations toward complete re-platforming for AI capabilities or cloud-based solutions, Microsoft’s approach with SQL Server 2025 stands out by enabling businesses to build on their existing investments and move forward with next-gen AI-based solutions. This represents an efficient path to implementing AI at scale for many enterprises.
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