SQL Server 2025 Adds Vector Search: AI Meets Data

Microsoft SQL Server 2025 became generally available on November 18, 2025 at Microsoft Ignite, bringing native vector search, RAG workflows, and Azure OpenAI integration directly into the database engine—all accessible via T-SQL. The release represents Microsoft’s bet that enterprises won’t migrate data to specialized vector databases like Pinecone or Weaviate when SQL Server can become the vector database itself. The strategic shift challenges the “move data to AI” paradigm with “bring AI to data.” Microsoft is betting enterprises will choose consolidation over specialization.

However, this isn’t just another database update. SQL Server 2025 directly competes with billion-dollar vector database startups by eliminating the need for separate AI infrastructure. Moreover, enterprises already invested in SQL Server can now build RAG applications, semantic search, and AI-powered features without learning new platforms or replicating data. Nevertheless, the strategic question remains: Will “good enough” integrated solutions beat “best in class” specialized tools?

DiskANN Brings Vector Search to SQL Server

SQL Server 2025 uses DiskANN, a Microsoft Research algorithm that creates graph-based vector indexes optimized for SSDs. Specifically, it maintains approximately 95% recall accuracy while handling vector datasets larger than available RAM, enabling semantic search, RAG workflows, and recommendation systems entirely within T-SQL. Furthermore, the implementation leverages SSD storage and minimal memory to balance performance with resource efficiency.

Consequently, developers can now create vector indexes on embedding columns, query with VECTOR_SEARCH(), and generate embeddings inline via AI_GENERATE_EMBEDDINGS()—all without external vector databases. For instance, DBI Services demonstrated this capability by building a complete RAG shopping assistant entirely in T-SQL. Additionally, Mediterranean Shipping Company uses SQL Server 2025 for cross-environment data consistency, flowing ship data between onshore data centers and cloud analytics platforms without replication.

-- Register Azure OpenAI model
CREATE EXTERNAL MODEL MyEmbeddingsModel
WITH (
    LOCATION = 'https://my-openai-endpoint.azure.com',
    API_FORMAT = 'Azure OpenAI',
    MODEL_TYPE = EMBEDDINGS,
    MODEL = 'text-embedding-3-large',
    CREDENTIAL = MyAiCredential
);

-- Create vector index
CREATE VECTOR INDEX idx_doc_embeddings
ON Documents(Embedding)
WITH (TYPE = DISKANN);

-- Semantic search query
SELECT TOP 10 DocumentID, Content,
    VECTOR_SEARCH(Embedding, @QueryEmbedding, 'cosine') AS Similarity
FROM Documents
ORDER BY Similarity DESC;

This eliminates a major architectural complexity for enterprises. Instead of managing SQL Server for relational data plus Pinecone or Weaviate for vector search, teams consolidate on SQL Server 2025. No data replication, no learning new query languages, no synchronization headaches. However, the question is whether performance matches the consolidation promise.

Integrated Platform vs Specialized Performance

SQL Server 2025 positions itself as “Enterprise AI without the Learning Curve”—the alternative to specialized vector databases. In contrast, Pinecone delivers sub-5ms P99 latency at billion-scale with its Pod v3 architecture. Similarly, Weaviate excels at hybrid search combining vector similarity, keyword matching, and metadata filtering. Meanwhile, SQL Server 2025 maintains 95% recall with DiskANN but hasn’t published latency benchmarks. The trade-off: T-SQL familiarity, enterprise governance, and zero data migration versus proven ultra-low latency and AI-first architecture.

Indeed, Microsoft’s strategy doesn’t claim to beat specialized vector databases on pure performance. Instead, the pitch is eliminating the need for them by delivering acceptable performance where enterprise data already lives. Therefore, for organizations with data already in SQL Server, teams skilled in T-SQL, and governance requirements demanding on-premises or hybrid deployment, SQL Server 2025 removes barriers. Nevertheless, applications demanding Pinecone’s sub-5ms latency at billion-vector scale may still require specialization.

This mirrors the NoSQL versus SQL debate and specialized tools versus general-purpose platforms. Consequently, the vector database startup ecosystem—Pinecone, Weaviate, Chroma—now faces competition from integrated platforms. Notably, Microsoft isn’t targeting AI-first applications; it’s targeting enterprises choosing between managing separate systems or consolidating on existing infrastructure. The market will determine whether consolidation wins.

Complete RAG Pipelines in T-SQL

SQL Server 2025 supports complete RAG workflows entirely within T-SQL: text chunking via AI_GENERATE_CHUNKS, embedding generation through AI_GENERATE_EMBEDDINGS, vector indexing with CREATE VECTOR INDEX, semantic search using VECTOR_SEARCH, and LLM invocation via sp_invoke_external_rest_endpoint. Furthermore, integration with Azure OpenAI, OpenAI, and Ollama enables production RAG without external orchestration frameworks.

Specifically, developers register AI models as first-class database objects via CREATE EXTERNAL MODEL, then invoke them in standard T-SQL queries. Moreover, NVIDIA partnered with Microsoft to demonstrate GPU-accelerated RAG workflows using NVIDIA Nemotron and SQL Server 2025, showcasing enterprise-grade deployment on Azure Cloud and Azure Local. Additionally, the platform supports Azure AI Foundry, Azure OpenAI Service, and local Ollama models for air-gapped on-premises scenarios.

RAG is becoming the standard pattern for enterprise AI applications—chatbots, semantic search, Q&A systems. SQL Server 2025 makes RAG accessible to SQL developers without requiring Python, LangChain, or external vector databases. Lower barriers to entry expand who can build AI applications. Microsoft’s bet: democratizing AI development matters more than optimizing for the performance ceiling specialized tools provide.

What SQL Server 2025 Can’t Do (Yet)

SQL Server 2025’s vector capabilities come with constraints. Tables with vector indexes become read-only—no data modification while the index exists. Consequently, this forces developers to drop the index, update data, then recreate the index, an expensive operation for applications requiring frequent vector updates. Additionally, vector indexes can’t be partitioned, limiting horizontal scale-out strategies for massive vector datasets. Tables must have single-column integer primary key clustered indexes.

Moreover, the vector data type doesn’t support traditional B-tree indexing, mathematical operations beyond similarity search, or compatibility with memory-optimized tables and Always Encrypted. Specifically, Microsoft recommends exact search for datasets under 50,000 vectors, switching to approximate nearest neighbor (DiskANN) only when datasets exceed this threshold. These limitations aren’t dealbreakers for typical use cases—RAG, semantic search, recommendations—but they’re critical for applications needing real-time vector updates or extreme scale.

Microsoft touts “great performance” but hasn’t published latency numbers to compare against Pinecone’s sub-5ms benchmarks. The read-only limitation is the most significant constraint, blocking adoption for applications requiring continuous vector updates without downtime. Developers evaluating SQL Server 2025 versus Pinecone or Weaviate must understand these trade-offs before committing. The platform delivers consolidation and familiarity, not best-in-class performance or operational flexibility.

Key Takeaways

• SQL Server 2025 brings vector search, RAG workflows, and AI model management directly into the database via T-SQL, eliminating the need for separate vector databases for many enterprise use cases.
• DiskANN vector indexing maintains 95% recall while optimizing for SSD storage, enabling semantic search and recommendations without external infrastructure—consolidation over specialization.
• The platform targets enterprises with data already in SQL Server, teams skilled in T-SQL, and governance requirements preventing cloud-only vector databases, not AI-first applications demanding sub-5ms latency at billion-scale.
• Critical limitations include read-only tables with vector indexes, no partitioning support, and lack of published performance benchmarks—evaluate constraints against use case requirements before adoption.
• Choose SQL Server 2025 for consolidation, enterprise governance, and hybrid deployment flexibility; choose Pinecone or Weaviate for proven ultra-low latency, AI-first architecture, and applications where performance ceiling matters more than integration simplicity.