When DBAs Should Consider Using SQL Server to Store Vector Data and If It Makes Sense As we continue to build more applications using AI embedded solutions, database professionals are increasingly being asked to support workloads that would have seemed outside their charter just a few years ago. One of the most significant shifts has been the rise of vector data (i.e. high-dimensional numerical representations of content like text, images, and audio) to be used in machine learning, natural language processing (NLP), recommendation systems, and with AI Agents. While vector databases like Pinecone, Weaviate, and FAISS have dominated the conversation in the past, DBAs responsible for relational database systems such as SQL Server may find themselves asking: Can we store and query vector data in SQL Server? With the introduction of SQL Server 2025, this is now a reality and it makes sense for the data to be close to the additional source data used by AI. So, the answer is yes, and may I add, sometimes, you should. Why Consider SQL Server for Vector Data?Relational databases aren’t going away. I know I’m preaching to the crowd here. In fact, they remain the bedrock of most enterprise environments and the introduction of open-source relational databases acquired by analytics and AI vendors should confirm this for anyone. RDBMS are known for their consistency, governance, security, and integration with business workflows. When AI and vector workloads emerge in these environments, DBAs have three strategic advantages by leveraging SQL Server: - Operational Cohesion and Reduced Complexity
Storing vector data in SQL Server allows teams to keep vector storage close to existing structured and unstructured data. Instead of building separate pipelines and syncing data between multiple platforms, the AI data lives where the business data lives. This avoids duplication, simplifies data governance, and reduces integration complexity. - Security and Compliance
SQL Server offers battle-tested features around authentication, encryption, auditing, and access control. Vector databases often lack mature RBAC and encryption-at-rest models. If you’re dealing with sensitive embeddings (e.g., vectors derived from customer conversations or financial documents), SQL Server’s security capabilities are a compelling reason to centralize. - Cost and Resource Optimization
New tools mean new infrastructure, training, and support overhead. Many teams already have SQL Server licenses, skilled administrators, and monitoring in place. Storing vectors as arrays or JSON in SQL Server tables can be a practical stopgap or even a long-term solution, especially if your similarity queries are relatively low volume or can be pre-processed. Use Cases Where SQL Server Makes Sense for Vectors- Search Enhancement: You’ve embedded product descriptions or support tickets and want to offer semantic search, but the volume is low or you can batch the comparisons.
- AI Feature Storage: Embeddings from third-party AI solutions are used as part of a larger ML pipeline, and you want to version and store them alongside metadata in your SQL Server-based data lakehouse.
- Prototyping and Experimentation: You’re evaluating AI use cases and don’t want to invest in a dedicated vector DB until there’s clear ROI.
What SQL Server Can Do, and What It Can’tLet’s be clear: SQL Server isn’t a purpose-built vector database. You won’t get out-of-the-box Approximate Nearest Neighbor (ANN) search or native support for high-performance cosine similarity indexing. However, you can: - Store vector arrays (e.g., as VARBINARY, NVARCHAR(MAX), or JSON arrays)
- Implement basic similarity functions using T-SQL (e.g., dot product, cosine similarity)
- Use full-text search or integrated ML with SQL Server ML Services for hybrid solutions
For moderate workloads, especially with pre-filtering (e.g., narrowing candidates before computing similarity), this is often good enough. Terminology used by Microsoft’s support site varies vs. what many in the industry reference, so take the time to read the documentation and test out the vector capabilities to ensure they meet the requirements. SummaryDBAs don’t need to chase every trend, but they do need to enable them securely and smartly. Using SQL Server to store and query vector data isn’t about turning your RDBMS into a deep-learning powerhouse. It’s about extending its value as a trusted, scalable data platform into the world of AI. When the use case fits, and especially when security, integration, and simplicity matter, SQL Server can and should be part of your vector strategy. Take the time to justify the opportunities with AI, as well as the additional risks of vector data inclusion to SQL Server. Peace Out, DBAKevlar Join the debate, and respond to the editorial on the forums |
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