Sql Server 2025 Vs Sql Server 2022 Whats New For Ai — SQL Server 2025 vs SQL Server 2022: What’s New for AI?

Introduction
SQL Server 2022 and SQL Server 2025: The Big Difference
AI in SQL Server 2022
What SQL Server 2025 Adds for AI
Vector Data Type in SQL Server 2025
What Is an Embedding?
Generating Embeddings Directly From SQL Server
Connecting SQL Server 2025 to AI Models
Vector Similarity Search
Vector Distance Functions
Vector Indexes and Approximate Nearest Neighbor Search
Building a Simple Semantic Search Example
AI Generated Embeddings With SQL Server 2025
AI and Retrieval Augmented Generation
SQL Server 2025 as a Vector Database
SQL Server 2022 vs SQL Server 2025 for AI Applications
What Happens to Traditional SQL in SQL Server 2025?
SQL Server 2022 Intelligent Query Processing Still Matters
SQL Server 2025 AI Architecture
Security Considerations for AI Workloads
Performance Considerations
When Should You Upgrade From SQL Server 2022 to 2025?
When SQL Server 2022 May Still Be Enough
Practical Migration Considerations
A Practical AI Use Case
Common Misunderstandings About SQL Server 2025 AI
SQL Server 2025 AI Limitations
Best Practices for Building AI Applications
SQL Server 2025 vs SQL Server 2022: Quick Comparison
Final Thoughts

1. Introduction

Artificial intelligence has changed the way applications interact with data.

Earlier, a typical application might work like this:

Application
     |
     v
SQL Query
     |
     v
SQL Server
     |
     v
Results

For example:

SELECT
    CustomerName,
    TotalSales
FROM CustomerSales
WHERE City = 'Delhi';

Modern AI applications work differently.

A user might ask:

“Find customers from Delhi who have purchased products similar to wireless headphones.”

That question is not simply a traditional SQL filtering problem.

The application may need to understand the meaning of “similar to wireless headphones.”

This is where vectors and embeddings become important.

SQL Server 2025 introduces major capabilities specifically aimed at AI applications, including a native VECTOR data type, vector distance functions, vector search, vector indexes and the ability to generate embeddings through AI models. Microsoft describes these capabilities as part of SQL Server’s support for intelligent applications and AI workloads. (Microsoft Learn)

SQL Server 2022, on the other hand, introduced important intelligent query processing and performance capabilities, but it did not provide the same native vector and embedding functionality inside the SQL Server engine.

That is the major difference this article will explore.

2. SQL Server 2022 and SQL Server 2025: The Big Difference

The easiest way to understand the difference is this:

Area	SQL Server 2022	SQL Server 2025
Traditional relational database	Yes	Yes
T-SQL	Yes	Yes
Query Store	Yes	Yes
Intelligent Query Processing	Yes	Yes
Parameter Sensitive Plan optimization	Yes	Yes
DOP feedback	Yes	Yes
Cardinality estimation feedback	Yes	Yes
Native `VECTOR` data type	No	Yes
Native vector distance functions	No	Yes
Native vector search	No	Yes
Vector indexes	No	Yes
`AI_GENERATE_EMBEDDINGS`	No	Yes
`CREATE EXTERNAL MODEL`	No	Yes
AI-oriented application development inside SQL Server	Limited	Much stronger

SQL Server 2025 adds a new AI-oriented layer on top of the traditional relational database engine.

Microsoft’s SQL Server 2025 documentation lists VECTOR_DISTANCE, VECTOR_NORM, VECTOR_NORMALIZE, VECTORPROPERTY, CREATE VECTOR INDEX, VECTOR_SEARCH and CREATE EXTERNAL MODEL among the new AI-related capabilities. (Microsoft Learn)

This does not mean SQL Server 2022 is obsolete.

SQL Server 2022 remains a powerful database platform with major improvements in query processing and workload performance.

The difference is that SQL Server 2025 makes the database itself much more useful for modern AI workloads.

3. AI in SQL Server 2022

Before looking at SQL Server 2025, it is important to understand what SQL Server 2022 already provided.

SQL Server 2022 introduced several intelligent query processing improvements.

Some important examples include:

Parameter Sensitive Plan optimization
Cardinality Estimation feedback
Degree of Parallelism feedback
Memory Grant feedback improvements
Query Store improvements
Optimized plan forcing

For example, Parameter Sensitive Plan optimization helps when the same parameterized query behaves very differently for different parameter values.

Consider:

SELECT
    *
FROM Orders
WHERE CustomerID = @CustomerID;

Suppose:

Customer 101
    5 orders

Customer 5000
    5,000,000 orders

One execution plan may not be ideal for both situations.

SQL Server 2022 can maintain multiple plans for parameter-sensitive queries in appropriate circumstances. Microsoft documents Parameter Sensitive Plan optimization as part of SQL Server 2022’s intelligent query processing improvements. (Microsoft Learn)

These capabilities are important for AI applications too because AI-generated queries still need to execute efficiently.

However, there is an important distinction:

SQL Server 2022 is intelligent about query execution, but it was not designed as a native vector database or AI application platform.

4. What SQL Server 2025 Adds for AI

SQL Server 2025 changes the picture significantly.

The important AI-related additions include:

VECTOR data type
        |
        v
Vector distance functions
        |
        v
Vector search
        |
        v
Vector indexes
        |
        v
AI-generated embeddings
        |
        v
External AI models

Let’s look at these one by one.

5. Vector Data Type in SQL Server 2025

A vector is essentially an ordered collection of numbers.

For example:

[0.15, -0.42, 0.91, 0.33]

In AI applications, these numbers can represent the meaning or characteristics of data.

SQL Server 2025 introduces a native VECTOR data type for storing vector data. Microsoft explains that vectors are stored in an optimized binary representation while being exposed as JSON arrays for convenience. (Microsoft Learn)

A simple example is:

DECLARE @v VECTOR(3) = '[0.1, 0.2, 0.3]';

SELECT @v AS VectorValue;

You can also create a vector from a JSON array:

SELECT
    CAST('[1.0, -0.2, 30]' AS VECTOR(3)) AS VectorValue;

This is a major change compared with SQL Server 2022.

Previously, developers often had to store embeddings in formats such as:

JSON
VARBINARY
XML
Multiple FLOAT columns

or use a separate vector database.

SQL Server 2025 provides a native vector representation.

6. What Is an Embedding?

To understand why vectors matter, we need to understand embeddings.

Suppose we have these sentences:

"I want to buy a laptop."

"I need a new notebook computer."

A traditional keyword search might see:

laptop

and:

notebook computer

as very different strings.

An embedding model attempts to represent the semantic meaning of the text as a vector.

For example, the actual vectors might look conceptually like:

"I want to buy a laptop."
        |
        v
[0.12, -0.83, 0.44, 0.19, ...]

and:

"I need a new notebook computer."
        |
        v
[0.11, -0.79, 0.46, 0.21, ...]

The vectors are not human-readable.

Their value comes from the mathematical relationships between them.

If two pieces of text have similar meanings, their vectors may be relatively close under an appropriate distance metric.

Microsoft describes embeddings as vectors that represent important features of data and can capture semantic similarity between concepts. (Microsoft Learn)

7. Generating Embeddings Directly From SQL Server

This is one of the most interesting SQL Server 2025 features.

SQL Server 2025 introduces:

AI_GENERATE_EMBEDDINGS()

The function generates an embedding by using an AI model that has been registered as an external model.

Microsoft documents the syntax as:

AI_GENERATE_EMBEDDINGS
(
    source
    USE MODEL model_identifier
)

and states that the function creates embedding vectors from character data. (Microsoft Learn)

Conceptually:

SELECT
    AI_GENERATE_EMBEDDINGS(
        N'Wireless headphones with noise cancellation'
        USE MODEL MyEmbeddingModel
    );

The result is an embedding vector.

Conceptually:

[0.018,
 -0.092,
  0.551,
 ...
]

The actual number of dimensions depends on the model.

This is powerful because the database can participate directly in the embedding workflow.

8. Connecting SQL Server 2025 to AI Models

How does SQL Server know which AI model to use?

SQL Server 2025 introduces:

CREATE EXTERNAL MODEL

This creates an external model object containing information about an AI model endpoint.

Microsoft documents support for API formats including:

Azure OpenAI
OpenAI
Ollama
ONNX Runtime

for embedding models. (Microsoft Learn)

For example, conceptually:

CREATE EXTERNAL MODEL MyEmbeddingModel
WITH
(
    LOCATION = 'https://your-endpoint/.../',
    API_FORMAT = 'Azure OpenAI',
    MODEL_TYPE = EMBEDDINGS,
    MODEL = 'your-embedding-model'
);

The exact configuration depends on your AI provider, endpoint, authentication method and environment.

This is important because SQL Server does not need to contain the AI model itself in every scenario.

Instead, SQL Server can communicate with an external model endpoint.

The architecture becomes:

                SQL Server 2025
                       |
                       |
              AI_GENERATE_EMBEDDINGS
                       |
                       v
              External AI Model
                       |
                       v
                  Embedding
                       |
                       v
                VECTOR column

SQL Server 2025 also supports local ONNX Runtime scenarios for embeddings, subject to the documented prerequisites and configuration. (Microsoft Learn)

9. Vector Similarity Search

Generating vectors is only half of the problem.

The next question is:

How do we find similar vectors?

Suppose our database contains product descriptions.

Product 1:
Wireless headphones with noise cancellation

Product 2:
Bluetooth earbuds with active noise cancellation

Product 3:
Office chair with adjustable height

Product 4:
Laptop with 16 GB RAM

The user searches:

“noise cancelling headphones”

We can generate an embedding for the search text.

Then we compare that vector with the product vectors.

Conceptually:

Search Vector
      |
      v
[0.18, -0.42, 0.71, ...]
      |
      |
      +----------+
      |          |
      v          v
Product 1     Product 2
similar       very similar
      |
      v
Product 3
not similar

This is called vector similarity search.

10. Vector Distance Functions

SQL Server 2025 provides functions for working with vectors.

One of the most important is:

VECTOR_DISTANCE()

For example:

SELECT
    VECTOR_DISTANCE(
        'cosine',
        @QueryVector,
        Embedding
    ) AS Distance
FROM ProductEmbeddings;

The distance tells us how close two vectors are according to the selected metric.

Microsoft documents support for vector distance calculations and common metrics such as cosine, Euclidean and dot product approaches. (Microsoft Learn)

For many semantic search scenarios, cosine distance is commonly used.

The basic idea is:

Smaller distance
       =
More similar

So we can sort by distance:

ORDER BY Distance;

11. Vector Indexes and Approximate Nearest Neighbor Search

Imagine you have:

1,000 products

A simple vector search may be perfectly acceptable.

But imagine:

10 million documents

Calculating the distance against every vector for every search can become expensive.

SQL Server 2025 introduces vector indexes and approximate nearest neighbor search capabilities.

Microsoft documents CREATE VECTOR INDEX for creating an approximate index on a vector column and VECTOR_SEARCH for searching similar vectors. (Microsoft Learn)

A simplified example is:

CREATE VECTOR INDEX IX_ProductEmbedding
ON dbo.ProductEmbeddings(Embedding)
WITH (METRIC = 'cosine');

Then vector search can be performed using VECTOR_SEARCH.

For example, Microsoft’s documented pattern includes:

SELECT TOP (10) WITH APPROXIMATE
    p.name,
    vs.distance
FROM products AS p
INNER JOIN VECTOR_SEARCH
(
    TABLE = product_embeddings AS e,
    COLUMN = embedding,
    SIMILAR_TO = @qv,
    METRIC = 'cosine'
) AS vs
    ON p.id = e.product_id
ORDER BY vs.distance;

The exact syntax and feature status should always be checked against the SQL Server 2025 build and current Microsoft documentation. Microsoft’s current documentation notes that vector indexes and VECTOR_SEARCH in SQL Server 2025 require the PREVIEW_FEATURES database scoped configuration. (Microsoft Learn)

This is an important distinction for production planning.

12. Building a Simple Semantic Search Example

Let’s create a simple product table.

CREATE TABLE dbo.Products
(
    ProductID INT PRIMARY KEY,
    ProductName NVARCHAR(200),
    Description NVARCHAR(MAX)
);

Now create a table to store embeddings:

CREATE TABLE dbo.ProductEmbeddings
(
    ProductID INT PRIMARY KEY,
    Embedding VECTOR(1536)
);

The number 1536 is only an example.

The vector dimension must match the embedding model being used.

Now suppose we have:

ProductID = 1

ProductName:
Noise Cancelling Headphones

Description:
Wireless headphones with active noise cancellation.

We generate an embedding:

INSERT INTO dbo.ProductEmbeddings
(
    ProductID,
    Embedding
)
SELECT
    1,
    AI_GENERATE_EMBEDDINGS(
        N'Wireless headphones with active noise cancellation'
        USE MODEL MyEmbeddingModel
    );

Now we have:

Product
   |
   +-- ProductID
   +-- Description
   |
   +-- Embedding

The database now contains both the business data and its semantic representation.

13. AI Generated Embeddings With SQL Server 2025

Let’s make the workflow more practical.

Suppose we have:

CREATE TABLE dbo.KnowledgeBase
(
    DocumentID INT PRIMARY KEY,
    Title NVARCHAR(500),
    Content NVARCHAR(MAX),
    Embedding VECTOR(1536)
);

We could generate embeddings from the document content.

Conceptually:

UPDATE dbo.KnowledgeBase
SET Embedding =
    AI_GENERATE_EMBEDDINGS(
        Content USE MODEL MyEmbeddingModel
    );

Now the table contains:

DocumentID
Title
Content
Embedding

This is extremely useful for applications such as:

Technical documentation search
Customer support
Internal knowledge bases
Product search
FAQ systems
Policy search
Document discovery

Instead of asking:

“Does this document contain the exact words I searched for?”

we can ask:

“Is this document semantically related to what I searched for?”

That is a much more powerful search model.

14. AI and Retrieval Augmented Generation

One of the most important applications of vectors is Retrieval Augmented Generation, commonly called RAG.

A simplified RAG architecture looks like this:

User Question
      |
      v
Generate Query Embedding
      |
      v
Vector Search
      |
      v
Find Relevant Documents
      |
      v
Send Documents + Question
      |
      v
Large Language Model
      |
      v
Generated Answer

Suppose a user asks:

“What is our policy for database backups?”

The system could:

Generate an embedding for the question.
Search the document embeddings.
Find the most relevant policy documents.
Send those documents to an LLM.
Ask the LLM to answer using those documents.

SQL Server 2025 can participate directly in the middle of this architecture.

That means you may not need a separate vector database for every application.

15. SQL Server 2025 as a Vector Database

This is one of the biggest architectural changes.

Before SQL Server 2025, an organization building an AI application might have had:

Application Database
        |
        v
SQL Server 2022
        |
        |
        +------> Vector Database
                    |
                    v
              AI Application

Now it can potentially become:

                SQL Server 2025
                      |
        +-------------+-------------+
        |             |             |
   Relational      Vectors      Metadata
      Data          Data
        |             |
        +-------------+-------------+
                      |
                      v
                AI Application

This can simplify architecture.

Instead of maintaining:

SQL Server
+
Vector database
+
Synchronization process
+
Additional security
+
Additional monitoring

you can potentially store relational data and vectors together.

Microsoft explicitly describes SQL Database Engine vector capabilities as useful when applications need to search structured and unstructured data together without introducing a separate search service. (Microsoft Learn)

However, this does not mean every vector workload should automatically move to SQL Server.

Architecture should still depend on:

Data volume
Search requirements
Latency requirements
Existing infrastructure
Cost
Operational complexity
AI model requirements

16. SQL Server 2022 vs SQL Server 2025 for AI Applications

Let’s compare them from an application developer’s perspective.

Capability	SQL Server 2022	SQL Server 2025
Store relational data	Excellent	Excellent
Traditional SQL queries	Excellent	Excellent
Query performance intelligence	Excellent	Further improved
Query Store	Yes	Yes
AI embeddings stored natively	No native VECTOR type	Yes
Native vector data type	No	Yes
Vector distance	No native vector functions	Yes
Semantic vector search	Requires external technology or custom implementation	Native vector capabilities
Vector indexing	No native vector index	Yes
AI embedding generation from T-SQL	No	Yes
External AI model object	No	Yes
RAG data retrieval	Usually requires additional components	Can be built directly around SQL Server vector capabilities
AI application integration	External services commonly required	Much more database-native

The biggest change is therefore not:

“SQL Server 2025 has a few more AI commands.”

The bigger change is:

SQL Server 2025 allows the database engine to become an active component of the AI application architecture.

17. What Happens to Traditional SQL in SQL Server 2025?

Nothing fundamental changes.

Your existing SQL remains important.

For example:

SELECT
    CustomerID,
    SUM(TotalAmount) AS TotalSales
FROM Orders
GROUP BY CustomerID;

is still normal SQL.

Indexes such as:

CREATE INDEX IX_Orders_CustomerID
ON Orders(CustomerID);

still matter.

Execution plans still matter.

Statistics still matter.

Query Store still matters.

The optimizer still matters.

SQL Server 2025 is not replacing relational database technology with AI.

Instead, it adds AI capabilities alongside the relational engine.

Think of it as:

SQL Server 2022
       |
       v
Relational + Intelligent Query Processing

SQL Server 2025
       |
       +---- Relational
       |
       +---- Intelligent Query Processing
       |
       +---- Vector Storage
       |
       +---- Vector Search
       |
       +---- Embeddings
       |
       +---- External AI Models

18. SQL Server 2022 Intelligent Query Processing Still Matters

It would be a mistake to think:

“SQL Server 2025 is about AI, so performance features are no longer important.”

The opposite is true.

AI applications can generate demanding queries.

For example, an AI application may execute:

SELECT TOP (10)
    DocumentID,
    VECTOR_DISTANCE(
        'cosine',
        @QueryVector,
        Embedding
    ) AS Distance
FROM Documents
ORDER BY Distance;

That query still runs inside the database engine.

The database engine’s ability to:

Optimize
Estimate
Allocate memory
Parallelize
Cache
Execute
Monitor

still matters.

SQL Server 2022 already introduced important intelligent query processing improvements such as Parameter Sensitive Plan optimization, DOP feedback and Cardinality Estimation feedback. (Microsoft Learn)

Therefore, SQL Server 2025’s AI capabilities should be viewed as an extension of the database engine, not a replacement for traditional SQL Server performance engineering.

19. SQL Server 2025 AI Architecture

A modern application could look like this:

                       User
                         |
                         v
                  AI Application
                         |
              +----------+----------+
              |                     |
              v                     v
       User Question          Application Data
              |                     |
              v                     |
       Generate Embedding            |
              |                     |
              +----------+----------+
                         |
                         v
                  SQL Server 2025
                         |
          +--------------+--------------+
          |              |              |
          v              v              v
     Relational       VECTOR        Metadata
       Tables         Columns
          |              |
          +--------------+
                         |
                         v
                  Vector Search
                         |
                         v
                 Relevant Records
                         |
                         v
                  AI Application
                         |
                         v
                       LLM
                         |
                         v
                  Final Answer

This architecture is particularly interesting for RAG applications.

20. Security Considerations for AI Workloads

AI features introduce new security considerations.

For example, if SQL Server can call an external AI endpoint, you need to control:

Who can call the model?
What data can be sent?
Which endpoint is allowed?
Which credentials are used?
Which users can access embeddings?

SQL Server 2025’s external model functionality supports database-level permissions.

For example:

GRANT CREATE EXTERNAL MODEL
TO [AI_Admin];

and access to an external model can be granted using:

GRANT EXECUTE
ON EXTERNAL MODEL::MyEmbeddingModel
TO [AI_User];

Microsoft documents these permissions for external models. (Microsoft Learn)

This is important because an embedding request can potentially send sensitive database information to an external AI service.

For example, avoid blindly doing:

SELECT
    AI_GENERATE_EMBEDDINGS(
        CompleteCustomerRecord
        USE MODEL MyEmbeddingModel
    )
FROM Customers;

if CompleteCustomerRecord contains sensitive information.

Instead, carefully decide what information should be sent to the model.

21. Performance Considerations

AI features do not remove the need for performance engineering.

Consider embeddings.

A vector might have:

1536 dimensions

and every dimension uses a 4-byte single-precision floating-point representation in the native vector format. (Microsoft Learn)

That means vectors can consume meaningful storage.

If you store:

10 million embeddings

you need to consider:

Storage
Memory
CPU
I/O
Index size
Index maintenance
Query latency

Vector search also introduces new performance tradeoffs.

There are two broad approaches:

Exact nearest neighbor search

and:

Approximate nearest neighbor search

Exact search aims to identify the true nearest vectors but can become expensive as the number of vectors grows.

Approximate search trades some retrieval accuracy for better scalability and speed.

Microsoft describes SQL Server’s approximate vector search as a way to improve scalability for large vector collections and documents the use of DiskANN-based vector indexes. (Microsoft Learn)

This is similar to a familiar database concept:

Faster search often requires the right index and an appropriate access strategy.

But vector indexes behave differently from traditional B-tree indexes, so database professionals need to learn the new concepts rather than treating a vector index as simply another B-tree index.

22. When Should You Upgrade From SQL Server 2022 to 2025?

There is no single answer.

The decision depends on your workload.

An upgrade becomes particularly interesting if your organization is planning:

Semantic Search

For example:

“Find products similar to this description.”

RAG

For example:

“Answer questions using our internal documentation.”

AI-powered Customer Support

For example:

“Find previous customer cases similar to this complaint.”

Document Search

For example:

“Find policies related to employee travel reimbursement.”

AI-assisted Analytics

For example:

“Find sales records related to customers discussing delayed delivery.”

In these scenarios, native vector functionality can simplify architecture.

23. When SQL Server 2022 May Still Be Enough

If your workload is primarily:

OLTP
Traditional reporting
Data warehousing
Stored procedures
Business applications
Traditional BI

you may not immediately need SQL Server 2025 solely because of AI.

For example, if your application runs:

SELECT
    CustomerID,
    COUNT(*) AS OrderCount,
    SUM(TotalAmount) AS TotalSales
FROM Orders
GROUP BY CustomerID;

there is no vector requirement here.

SQL Server 2022 remains capable of handling such workloads.

In fact, upgrading a production SQL Server should never be based simply on:

“The newer version has AI.”

You should consider:

Business requirements
Application compatibility
Performance
Licensing
Hardware
Operating system
Third-party tools
Backup and monitoring
High availability
Disaster recovery
Security
Testing effort

24. Practical Migration Considerations

Suppose you currently have:

SQL Server 2022

and want to move to:

SQL Server 2025

Do not immediately start rewriting the application around vector search.

A better approach is:

Step 1: Upgrade the database engine

Validate existing workloads first.

Step 2: Test compatibility

Check:

Applications
Drivers
ETL tools
Monitoring
Backup tools
Third-party applications
SQL Agent jobs
Linked servers

Step 3: Identify AI use cases

Ask:

Where could semantic search or embeddings actually help the business?

Step 4: Build a small proof of concept

For example:

100,000 support documents
        |
        v
Generate embeddings
        |
        v
Store vectors
        |
        v
Semantic search
        |
        v
Measure quality and performance

Step 5: Measure

Check:

Search accuracy
Latency
Storage
CPU
Memory
Cost
Security

Step 6: Expand only after validation

This is especially important because vector search introduces new operational considerations.

25. A Practical AI Use Case

Let’s consider an internal IT support database.

Suppose we have:

CREATE TABLE dbo.SupportArticles
(
    ArticleID INT PRIMARY KEY,
    Title NVARCHAR(500),
    Content NVARCHAR(MAX),
    Embedding VECTOR(1536)
);

An employee asks:

“My SQL Server queries suddenly became very slow after a deployment.”

A traditional keyword search might look for:

SQL Server
slow
deployment

But an embedding-based search can potentially find documents containing concepts such as:

Query performance regression
Execution plan changes
Parameter sniffing
Statistics changes
Plan cache
Query Store

even if the exact words from the question do not appear.

This is where semantic search becomes powerful.

The workflow is:

User Question
     |
     v
"My SQL queries became slow after deployment"
     |
     v
Embedding Model
     |
     v
Query Vector
     |
     v
SQL Server 2025
     |
     v
Vector Similarity Search
     |
     v
Relevant Articles
     |
     v
LLM
     |
     v
Answer

This is a realistic AI architecture that can be built around SQL Server 2025’s vector capabilities.

26. Common Misunderstandings About SQL Server 2025 AI

Misunderstanding 1: SQL Server 2025 is an LLM

No.

SQL Server 2025 is still a database engine.

It can integrate with AI models and provide AI-oriented data capabilities.

Misunderstanding 2: Every AI model runs inside SQL Server

No.

SQL Server 2025 can connect to external AI model endpoints.

It also supports local ONNX Runtime scenarios for embeddings with the documented setup. (Microsoft Learn)

Misunderstanding 3: Vectors replace relational tables

No.

Vectors complement relational data.

You may have:

CustomerID
CustomerName
City

alongside:

CustomerEmbedding

Both can be useful.

Misunderstanding 4: Vector search replaces normal SQL

No.

You will often combine them.

For example:

Find documents semantically similar to the question
AND
only from the HR department
AND
only documents marked Active
AND
only documents updated after 2025

This combines vector search with traditional relational filtering.

Misunderstanding 5: SQL Server 2022 cannot participate in AI applications

It can.

You can build AI applications using SQL Server 2022 and external services.

The difference is that SQL Server 2025 provides much more native support for the vector and embedding parts of the architecture.

27. SQL Server 2025 AI Limitations

SQL Server 2025’s AI capabilities are significant, but they are not magic.

There are several things to consider.

Vector quality depends on the embedding model

A poor embedding model can produce poor search results.

Similarity does not mean correctness

Two documents can be semantically similar but still answer different questions.

Approximate search can trade accuracy for speed

This needs to be tested for your workload.

AI model calls can add latency

If SQL Server calls an external endpoint, network latency becomes part of the workflow.

AI model endpoints have their own costs

Using an external embedding service can incur charges.

Security becomes more important

Sensitive data may leave the database when sent to an external model.

Vector storage can become large

Millions of embeddings can consume substantial storage.

AI does not remove the need for database tuning

You still need:

Indexes
Statistics
Query Store
Execution plans
Memory management
CPU monitoring
I/O monitoring

28. Best Practices for Building AI Applications

If you are planning an AI application on SQL Server 2025, consider these practices.

1. Start with a business problem

Do not start with:

“We should use vectors.”

Start with:

“What problem are we trying to solve?”

2. Keep relational and vector data together when it makes sense

For example:

DocumentID
Title
Department
CreatedDate
Content
Embedding

This can make filtering and semantic retrieval work together.

3. Choose the embedding model carefully

The model determines the quality and characteristics of your embeddings.

4. Use appropriate dimensions

Do not arbitrarily choose:

VECTOR(1536)

The dimension should match the model output.

5. Test exact versus approximate search

For smaller datasets, exact search may be sufficient.

For larger datasets, approximate search and vector indexes may become important.

Microsoft’s documentation gives a general recommendation that exact nearest-neighbor search can be suitable when the search set is relatively small, while approximate methods are useful for larger-scale scenarios. (Microsoft Learn)

6. Combine vector filtering with traditional indexes

Suppose you need:

Similar products
AND Category = 'Electronics'
AND IsActive = 1

Traditional indexes can still help with filtering.

Microsoft documents scenarios where vector indexes and traditional indexes can work together for filtered vector searches. (Microsoft Learn)

7. Secure external model access

Control:

Credentials
Permissions
Endpoints
Data sent to models
Model access

8. Monitor AI workloads

Monitor both:

Database performance

and:

AI model performance

because your application now has two performance domains.

9. Keep an eye on feature status

This is particularly important for SQL Server 2025.

The current Microsoft documentation identifies vector indexes and VECTOR_SEARCH in SQL Server 2025 as requiring PREVIEW_FEATURES. Preview features should be evaluated carefully before production use. (Microsoft Learn)

Always check the documentation for the exact SQL Server 2025 build you are running.

29. SQL Server 2025 vs SQL Server 2022: Quick Comparison

Here is the simplest way to remember the difference.

Question	SQL Server 2022	SQL Server 2025
Is it a relational database?	Yes	Yes
Does it support advanced query processing?	Yes	Yes
Does it support intelligent query processing?	Yes	Yes
Does it support Query Store improvements?	Yes	Yes
Can it work with AI applications?	Yes, mainly through external components	Yes, with significantly more native capabilities
Native vector data type?	No	Yes
Native vector distance functions?	No	Yes
Native vector search?	No	Yes
Native vector indexes?	No	Yes
Generate embeddings from T-SQL?	No	Yes
Register external AI models?	No	Yes
Suitable for RAG architectures?	Yes, with additional components	Yes, with native vector capabilities
Can relational and vector data live together?	Requires custom/external approach	Yes
Best reason to upgrade for AI	Limited	Native AI application capabilities

30. Final Thoughts

The difference between SQL Server 2022 and SQL Server 2025 is not simply a list of new T-SQL commands.

The more important change is architectural.

SQL Server 2022 is primarily a highly capable relational database platform with intelligent query processing and strong performance capabilities.

SQL Server 2025 keeps all of that and adds capabilities that make it much more suitable for AI-powered applications.

The most important new concepts are:

VECTOR
   |
   v
Embeddings
   |
   v
AI_GENERATE_EMBEDDINGS
   |
   v
Vector Distance
   |
   v
Vector Search
   |
   v
Vector Index
   |
   v
Semantic Search
   |
   v
RAG Applications

For example, instead of storing only:

DocumentID
Title
Content

you can now have:

DocumentID
Title
Content
Embedding

and search using both traditional SQL and semantic similarity.

That opens the door to applications such as:

AI-powered document search
Semantic product search
Customer support assistants
Enterprise knowledge assistants
RAG applications
AI-powered recommendation systems
Semantic incident search
Intelligent SQL Server troubleshooting assistants

The biggest takeaway is therefore:

SQL Server 2025 does not replace traditional SQL. It extends SQL Server so that relational data, embeddings and AI-oriented search can work much more closely together.

For a SQL Server professional who already understands:

Indexes
Execution Plans
Query Store
Statistics
T-SQL
Performance Tuning
Security
Data Modeling

SQL Server 2025 adds another important area to learn:

Embeddings
Vectors
Similarity Search
Vector Indexes
AI Models
RAG
Semantic Search

This makes SQL Server 2025 particularly interesting for database professionals who want to move from traditional database administration and performance tuning toward AI-enabled database engineering.

One final caution is important. SQL Server 2025’s AI capabilities are evolving, and Microsoft currently identifies some vector indexing and approximate vector search functionality as preview features in SQL Server 2025. Therefore, before implementing these features in production, verify the exact feature status, limitations and syntax for your installed SQL Server 2025 build. (Microsoft Learn)

For someone coming from SQL Server 2022, the most valuable learning path is not to abandon traditional SQL Server knowledge. It is to build on it:

SQL Server Fundamentals
        ↓
Performance Tuning
        ↓
Query Store and Intelligent Query Processing
        ↓
SQL Server 2025
        ↓
Vector Data
        ↓
Embeddings
        ↓
Vector Search
        ↓
RAG
        ↓
AI-Powered Database Applications

That is where SQL Server is heading: not just storing data for AI applications, but becoming an active part of the AI application itself.

In the next few days, I’ll be publishing separate articles covering topics such as Vector Databases, Vector Data Types, Vector Search, Vector Indexes, Vector Distance, and Embeddings in SQL Server. Each article will explain these concepts in a simple and practical way to help you build a better understanding of SQL Server’s AI and vector capabilities.

Keep following TechMixing.com for more practical SQL Server, Azure, Power BI, AI, and data content!

Read more articles on SQL server & Azure SQL