Vector Search in Azure SQL: An Architecture Guide for Enterprise RAG Applications

Azure SQL and SQL Server 2025 bring vector search into the database: the VECTOR data type stores embeddings next to business data, the VECTOR_DISTANCE function runs similarity searches, and DiskANN-based vector indexes speed up approximate search on large data sets. That makes it possible to start enterprise RAG (Retrieval-Augmented Generation) applications without a separate vector database. But the architecture decision isn’t made on the data type alone: scale, hybrid search, embedding cost and personal data protection have to be weighed together.

This guide is for architects, development teams and data platform managers who want to connect enterprise data to AI applications. We tested the code samples on SQL Server 2025.

Vector Support in SQL Server and Azure SQL at a Glance

Status as of November 2026, per Microsoft’s documentation; check the current status before using preview features in production.

Key Concepts

Example: Storing Vectors and Running a Similarity Search

The example below uses three-dimensional vectors to show the idea. In real applications, embedding models usually produce vectors with hundreds or thousands of dimensions.

CREATE TABLE dbo.Documents (
    Id int IDENTITY PRIMARY KEY,
    Title nvarchar(200) NOT NULL,
    Embedding vector(3) NOT NULL
);

INSERT INTO dbo.Documents (Title, Embedding) VALUES
  (N'Backup policy', '[0.9, 0.1, 0.0]'),
  (N'Restore test',  '[0.8, 0.2, 0.1]'),
  (N'Sales report',  '[0.0, 0.1, 0.9]');

DECLARE @q vector(3) = '[0.85, 0.15, 0.05]';

SELECT TOP (2) Title,
       VECTOR_DISTANCE('cosine', @q, Embedding) AS distance
FROM dbo.Documents
ORDER BY distance;
-- Backup policy and Restore test come first

Microsoft recommends exact search (with VECTOR_DISTANCE) when fewer than 50,000 vectors are searched. The table can hold many more rows; what matters is that the WHERE conditions keep the number of vectors entering the search below that limit. For larger data sets, approximate search uses a vector index (CREATE VECTOR INDEX) and the VECTOR_SEARCH function.

Architecture Decision 1: Azure SQL or PostgreSQL with pgvector?

As a rule, starting on the platform where the data already lives is the lowest-risk choice: copying embeddings to a separate system brings synchronization, permission management and consistency problems. We cover the PostgreSQL side in our What Is PostgreSQL? post.

Architecture Decision 2: When Do You Need a Separate Vector Database?

Vector support in a relational database is enough for most enterprise RAG scenarios. Consider a separate vector database when:

• The number of vectors reaches hundreds of millions and the search load is very high.
• You need advanced features specific to vector search (multi-vector search, special ranking methods).
• Vector search is heavy enough to affect the performance of the business database.

A separate system means managing the permission model and the data in two places. Before taking on that burden, measure whether you’ve really reached the limits of the relational database.

Architecture Decision 3: Hybrid Search

Vector search finds content that is similar in meaning, but it can be weak for searches that need an exact match, such as a product code, a contract number or a proper name. Hybrid search combines the results of full-text search and vector search:

Because full-text search and vector search can run in the same SQL Server database, hybrid search doesn’t require spreading data across separate systems. The two result lists are usually merged into one with a rank-based fusion method (for example Reciprocal Rank Fusion).

Embedding Cost and Storage

Data Protection: What Shouldn’t Be Vectorized?

Embeddings carry the meaning of the source text; the embedding of a text containing personal data is tied to personal data too. RAG systems also send the retrieved content to a language model. So:

• Leave out personal data you don’t need: national ID numbers, contact details and special categories such as health data should be removed or masked from the content you vectorize if they aren’t needed to produce answers.
• Carry access control into search: users should see only chunks of documents they’re authorized for. Mechanisms such as Row-Level Security must apply to vector search too; otherwise RAG becomes a leak channel that crosses permission boundaries.
• Assess where the model service runs: if the language model and embedding service are abroad, the context you send may count as a cross-border transfer under data protection law.
• Keep records: which user reached which content with which question must be auditable.

An example from the field: a RAG application fed with unmasked data could show users information in its answers that they normally weren’t allowed to see. We discuss risks like this in our data governance post, and the security controls in Azure SQL in our Azure SQL KVKK compliance guide.

Enterprise RAG Architecture with Aryasoft

Aryasoft designs the database layer for your RAG applications: where the data lives, how vector search runs, how access control carries into search and how personal data is protected. With our experience in SQL Server, Azure SQL and PostgreSQL, we recommend an architecture that starts from your existing data platform with the fewest changes. See our choosing a database for RAG post, where we cover the platform choice in detail, and our data analytics and AI services.

Frequently Asked Questions

Sources

The technical details in this guide were checked against the following official documentation in November 2026.

1. Microsoft Learn: Vector Search and Vector Index
2. Microsoft Learn: Vector data type
3. Microsoft Learn: VECTOR_DISTANCE
4. Microsoft Learn: CREATE VECTOR INDEX
5. Microsoft Learn: VECTOR_SEARCH
6. pgvector