Azure SQL Can Generate Embeddings Now — In Pure T-SQL, No App Layer Needed

Emiliano Montesdeoca — Fri, 22 May 2026 00:00:00 +0000

If you’ve ever built a RAG pipeline, you know the pipeline tax: your data lives in SQL, but to generate embeddings you need to extract it, call an embedding API, handle batching and rate limits, and store the results somewhere vector-searchable. Often in a different database entirely.

Azure SQL just removed most of that with two features that are now generally available: CREATE EXTERNAL MODEL and AI_GENERATE_EMBEDDINGS.

What They Do

These two T-SQL features work as an integrated pipeline:

CREATE EXTERNAL MODEL — registers an external AI model endpoint as a named database object. You set the location, API format, model type, and credentials once. Reuse it everywhere.

AI_GENERATE_EMBEDDINGS — a scalar T-SQL function that calls the registered model and returns a JSON array of vector values. Works in SELECT, INSERT, UPDATE, and MERGE statements.

Together they form an end-to-end embedding pipeline without leaving the SQL engine.

The Complete Workflow

-- Step 1: Register your embedding provider once
CREATE EXTERNAL MODEL MyEmbeddingModel
WITH (
 LOCATION = 'https://your-aoai-resource.openai.azure.com/',
 API_FORMAT = 'Azure OpenAI',
 MODEL_TYPE = EMBEDDINGS,
 MODEL = 'text-embedding-ada-002'
);

-- Step 2: Generate embeddings inline in T-SQL
UPDATE docs
SET embedding = AI_GENERATE_EMBEDDINGS(content USE MODEL MyEmbeddingModel)
FROM documents AS docs;

-- Step 3: Search with vector distance
SELECT TOP 10 id, content
FROM documents
ORDER BY VECTOR_DISTANCE('cosine', embedding,
 AI_GENERATE_EMBEDDINGS(@query USE MODEL MyEmbeddingModel));

That’s the whole pipeline: data in SQL, embeddings generated in SQL, similarity search in SQL. No orchestration layer, no ETL, no separate vector database.

Supported API Formats and Options

At GA, API_FORMAT supports Azure OpenAI and OpenAI. MODEL_TYPE is locked to EMBEDDINGS for now. The PARAMETERS JSON lets you set model-level defaults including retry count:

PARAMETERS = '{"sql_rest_options":{"retry_count":3}}'

Authentication uses database credentials, so secrets stay out of your application code.

What This Enables for .NET Applications

For .NET developers building AI features on top of existing SQL data, this is significant. You don’t need to:

Extract data to an intermediate store for embedding
Manage an external embedding pipeline
Set up a separate vector database (though you can use Azure AI Search if you want a full-featured vector store)
Change your application’s data access layer

You can add semantic search to existing SQL applications incrementally, using the same T-SQL tooling you already have.

Wrapping Up

RAG patterns on SQL data just got dramatically simpler. AI_GENERATE_EMBEDDINGS + CREATE EXTERNAL MODEL means your existing SQL application can gain vector search capabilities without adding new infrastructure.

Both features are GA in Azure SQL Database and Azure SQL Managed Instance today.