Vector Index in SQL Server 2025: Examples & Best Practices

Introduction

SQL Server 2025 introduces vector search capabilities that help developers build AI-powered applications using data stored in SQL Server. One of the important concepts to understand is the vector index: a structure designed to help find similar vectors without comparing a query vector against every vector in a table.

Imagine an e-commerce website where a customer searches for “comfortable running shoes for long-distance running.” The customer might not use the exact words stored in the product catalog. Traditional keyword searches can miss relevant products, but vector search can help identify products with similar meaning.

In this article, we’ll explore what a vector index is, how it works, where it is useful, and how to build a practical example in SQL Server 2025.

1. What Is a Vector Index?

A vector is a collection of numerical values that represents information such as a product description, document, image, or customer review. AI embedding models convert this information into vectors that capture aspects of its meaning.

For example, an embedding model might represent these descriptions as vectors:

• “Comfortable shoes for marathon running”
• “Lightweight footwear for long-distance runners”
• “Wireless headphones with noise cancellation”

The first two descriptions may have vectors that are closer to each other than either is to the third description.

A vector index helps SQL Server find nearby vectors efficiently. SQL Server 2025 supports vector indexing using the DiskANN approximate nearest-neighbor search approach. Instead of calculating the distance from a query vector to every stored vector, an approximate search can identify likely nearest neighbors more efficiently.

Traditional index vs. vector index

These indexes solve different problems and can complement each other. A traditional index can filter by category or status, while a vector index helps rank semantically similar content.

2. How Does Vector Search Work?

The vector index does not generate embeddings or understand natural language by itself. The embedding model creates the numerical representation; SQL Server stores the vector and performs similarity search.

3. Real-World Use Cases

Use case 1: E-commerce product recommendations

A customer searches for “shoes for standing all day.” Vector search can retrieve descriptions about cushioned work shoes, supportive footwear, or comfort trainers even if they don’t contain the exact search phrase.

Use case 2: Internal knowledge assistant

An employee asks, “How do I recover a database after a failed deployment?” A vector search can retrieve relevant runbooks and incident documents that discuss restoring backups or recovering after deployment failures.

Use case 3: Similar support tickets

A support team can find previously resolved tickets with similar symptoms, even if customers used different words to describe the same problem.

These scenarios work best when the embedding model represents the domain well and the source text is clean and informative. Vector similarity is a relevance signal, not a guarantee that two records mean exactly the same thing.

4. SQL Server 2025 Vector Index: Practical Implementation

Let’s build a small product-search example. The objective is to store product descriptions and their embeddings, create a vector index, and retrieve similar products.

Important version and permission notes

• The VECTOR data type is available in SQL Server 2025.
• As of October 2026, CREATE VECTOR INDEX and VECTOR_SEARCH remain preview features in SQL Server 2025. Enable the database-scoped preview configuration before using them.
• You need appropriate permissions to alter database-scoped configuration, create tables and indexes, and insert or query data. In a production environment, use your organization’s approved permissions model.
• The latest vector index format requires at least 100 rows before index creation.

Step 1: Enable the Preview Feature

Run this in your test database on SQL Server 2025:

ALTER DATABASE SCOPED CONFIGURATION
SET PREVIEW_FEATURES = ON;
GO

Do not enable preview features in production without reviewing the current support status, known limitations, and your organization’s change-management policy.

Step 2: Create a Product Table

For this demonstration, we’ll use a five-dimensional vector so that the data is easy to read.

Real embedding models often produce hundreds or thousands of dimensions. The number of dimensions in the column must match the embedding model’s output.

CREATE TABLE dbo.ProductCatalog
(
    ProductID   INT           NOT NULL PRIMARY KEY,
    ProductName NVARCHAR(100) NOT NULL,
    Category    NVARCHAR(50)  NOT NULL,
    Description NVARCHAR(500) NOT NULL,
    Embedding   VECTOR(5)     NOT NULL
);
GO

The Embedding column stores the vector representation of each product description.

Step 3: Insert Demonstration Data

For a simple, repeatable index demonstration, we’ll generate 100 rows with synthetic vectors.

INSERT INTO dbo.ProductCatalog
(
    ProductID,
    ProductName,
    Category,
    Description,
    Embedding
)
SELECT
    gs.value,
    CONCAT(N'Demo Product ', gs.value),
    CASE
        WHEN gs.value % 3 = 0 THEN N'Sports'
        WHEN gs.value % 3 = 1 THEN N'Electronics'
        ELSE N'Home'
    END,
    CONCAT(N'Synthetic demonstration product ', gs.value),
    CAST(
        JSON_ARRAY(
            CAST(gs.value * 0.01 AS FLOAT),
            CAST(gs.value * 0.02 AS FLOAT),
            CAST(gs.value * 0.03 AS FLOAT),
            CAST(gs.value * 0.04 AS FLOAT),
            CAST(gs.value * 0.05 AS FLOAT)
        )
        AS VECTOR(5)
    )
FROM GENERATE_SERIES(1, 100) AS gs;
GO

Verify the inserted rows:

SELECT COUNT(*) AS ProductCount
FROM dbo.ProductCatalog;

Expected result: 100.

Important: These vectors are synthetic. They let you test the SQL syntax and index workflow, but they do not represent the semantic meaning of the product descriptions. For meaningful product recommendations, generate embeddings using a suitable embedding model and store the corresponding vectors.

Step 4: Create the Vector Index

CREATE VECTOR INDEX IX_ProductCatalog_Embedding
ON dbo.ProductCatalog (Embedding)
WITH
(
    METRIC = 'cosine',
    TYPE = 'DiskANN'
);
GO

What do these options mean?

Option	Purpose
METRIC = 'cosine'	Defines the distance metric used to compare vectors.
TYPE = 'DiskANN'	Selects the approximate nearest-neighbor index algorithm.
MAXDOP	Optional setting to limit parallelism during index creation.

DiskANN helps the database find approximate nearest neighbors efficiently. The chosen metric must match the metric used by the search query for the vector index to be eligible.

Step 5: Search for Similar Products

Now let’s find the three closest vectors to a query vector.

DECLARE @QueryVector VECTOR(5) =
    '[0.30, 0.30, 0.30, 0.30, 0.30]';

SELECT TOP (3) WITH APPROXIMATE
    p.ProductID,
    p.ProductName,
    p.Category,
    p.Description,
    s.distance
FROM VECTOR_SEARCH
(
    TABLE = dbo.ProductCatalog AS p,
    COLUMN = Embedding,
    SIMILAR_TO = @QueryVector,
    METRIC = 'cosine'
) AS s
ORDER BY s.distance;

The query returns up to three approximate nearest-neighbor matches.

• A smaller distance indicates a closer match under the selected metric.
• WITH APPROXIMATE explicitly requests approximate nearest-neighbor search using the current vector-index syntax.
• ORDER BY s.distance returns the closest results first.

The query vector is also synthetic, so these results demonstrate the mechanics of vector search not a meaningful recommendation for any actual product. The current VECTOR_SEARCH syntax and ordering requirements are documented by Microsoft.

5. How to Make This a Real AI-Powered Product Search

In a real application, the workflow is slightly different.

SQL Server supports embedding generation through AI_GENERATE_EMBEDDINGS when a compatible model is configured. Alternatively, your application can generate embeddings using an external AI service and pass them to SQL Server.

For example, a real product-search query could use a vector generated from the question:

“I need comfortable shoes for standing at work all day.”

The embedding model converts that question into a vector. SQL Server searches the product embeddings, and the application displays the closest matches along with their names, prices, availability, and other metadata.

6. Choosing the Right Distance Metric

SQL Server supports three vector distance metrics for vector indexing and search.

Metric	Typical interpretation
cosine	Compares vector direction and is commonly used for text embeddings.
euclidean	Measures straight-line distance between vectors.
dot	Uses the negative dot product as the distance value.

Do not choose a metric solely because it is popular. Follow the embedding model’s recommendations and evaluate search quality using representative queries.

7. Vector Index vs. Exact Vector Search

A vector index is not always necessary.

For a small dataset, an exact search can be a sensible choice:

DECLARE @QueryVector VECTOR(5) =
    '[0.30, 0.30, 0.30, 0.30, 0.30]';

SELECT TOP (3)
    ProductID,
    ProductName,
    VECTOR_DISTANCE(
        'cosine',
        @QueryVector,
        Embedding
    ) AS distance
FROM dbo.ProductCatalog
ORDER BY distance;

This calculates the distance for the rows in the table and selects the closest three. It is an exact nearest-neighbor approach, unlike approximate vector search.

Microsoft recommends considering exact search for relatively small vector datasets; its documentation gives fewer than 50,000 vectors as a general guideline, depending on how many rows remain after filtering. Actual performance depends on the workload and hardware.

For larger datasets with frequent similarity searches, benchmark an approximate vector index and compare latency, resource usage, and recall against exact search.

8. Best Practices for DBAs and Database Architects

1. Choose the right embedding model. Search quality depends heavily on the representation of your source data.
2. Keep vector dimensions consistent. The stored vector and query vector must use compatible dimensions and representations.
3. Use a consistent metric. The index and search query should specify the same metric.
4. Retain relational metadata. Use normal SQL columns for product IDs, tenant IDs, categories, status, and permissions.
5. Apply security filters carefully. Semantic similarity must not bypass row-level access or tenant-isolation rules.
6. Test approximate search quality. Compare results with exact search using representative queries and measure recall.
7. Monitor index health and maintenance. Microsoft provides vector-index metadata and monitoring facilities; check the documentation for the applicable index version.
8. Plan for data changes. Current vector-index versions support DML with automatic index maintenance, but verify the capabilities and limitations of the specific version you’re running.

Common Mistakes to Avoid

• Treating vector search as a replacement for all traditional indexes.
• Expecting synthetic or random vectors to produce meaningful semantic results.
• Assuming approximate nearest-neighbor search always returns the mathematically closest rows.
• Using a different embedding model or incompatible dimensions for queries and stored data.
• Assuming a vector index automatically generates embeddings.
• Enabling a preview feature without evaluating operational and support implications.
• Forgetting to enforce application authorization and data-access rules on retrieved records.

9. Pro Tip: Combine Vector Search with Traditional SQL Filtering

Imagine an internal knowledge assistant that searches thousands of technical documents.

Vector search can locate documents related to “database recovery after a failed deployment.” Traditional SQL predicates can restrict results to approved documents, a particular category, or the user’s permitted tenant.

The latest vector-index implementation supports iterative filtering, where supported predicates are applied during the search process rather than only after candidates are returned. This can improve filtered search behavior, but verify the exact semantics and query plan for your SQL Server version.

10. Conclusion

A vector index in SQL Server 2025 helps applications find semantically related content by searching the embeddings stored in the database. It brings approximate nearest-neighbor search closer to the relational data and can simplify architectures for applications that need both structured queries and AI-powered retrieval.

For DBAs and database architects, the key is to understand the complete workflow: generate meaningful embeddings, store them correctly, select a suitable distance metric, create the vector index, and validate search quality and performance.

A vector index helps SQL Server find similar vectors efficiently but meaningful AI search still depends on good embeddings, appropriate filtering, and measured results.