AI in SQL Server vs PostgreSQL: Vector Search, Embeddings, and RAG Compared

Both SQL Server and PostgreSQL are moving fast into AI territory — and if you manage either (or both), you've probably wondered how they compare when it comes to storing embedding, running vector searches, and building RAG pipelines. The short answer: the capabilities are converging, but the approach is very different. Here's a practical breakdown.

1. The VECTOR data type: built-in vs extension

SQL Server 2025 ships with a native VECTOR data type, part of the core engine — no extra install needed. It stores embedding as optimized binary, surfaced as JSON arrays, with support for up to 1,998 dimensions. See the official type reference for the full syntax.

PostgreSQL takes the extension route via pgvector. You install it once and get a vector type that integrates natively with your existing tables. It's open source, available on RDS, Azure Database for PostgreSQL, Google Cloud SQL, and self-hosted instances.

-- SQL Server 2025
CREATE TABLE articles (
    id        INT PRIMARY KEY,
    title     NVARCHAR(500),
    embedding VECTOR(1536)
);
 
-- PostgreSQL + pgvector
CREATE EXTENSION IF NOT EXISTS vector;
 
CREATE TABLE articles (
    id        SERIAL PRIMARY KEY,
    title     TEXT,
    embedding VECTOR(1536)
);

2. Similarity search: VECTOR_DISTANCE vs the <=> operator

SQL Server 2025 uses the VECTOR_DISTANCE() function with explicit metric names ('cosine', 'euclidean', 'dot'). PostgreSQL with pgvector uses operator syntax: <=> for cosine, <-> for L2 (Euclidean), <#> for negative inner product. Both let you combine similarity with regular WHERE filters in the same query.

-- SQL Server 2025: top 5 similar articles
SELECT TOP 5
    title,
    VECTOR_DISTANCE('cosine', embedding, @query_vector) AS distance
FROM   articles
ORDER BY distance ASC;
 
-- PostgreSQL + pgvector: top 5 similar articles
SELECT
    title,
    embedding <=> $1 AS distance
FROM   articles
ORDER BY distance
LIMIT  5;

3. Approximate nearest neighbor (ANN) indexing

For large datasets, exact KNN scans get expensive. SQL Server 2025 introduces DiskANN-powered vector indexes via CREATE VECTOR INDEX (currently in preview, requires enabling PREVIEW_FEATURES). pgvector supports two ANN index types: ivfflat (IVF with flat quantization) and hnsw (Hierarchical Navigable Small Worlds). HNSW is generally preferred for query latency.

-- SQL Server 2025: DiskANN vector index (preview)
CREATE VECTOR INDEX idx_articles_embedding
    ON articles (embedding)
    WITH (METRIC = 'cosine');
 
-- PostgreSQL: HNSW index via pgvector
CREATE INDEX idx_articles_embedding
    ON articles
    USING hnsw (embedding vector_cosine_ops)
    WITH (m = 16, ef_construction = 64);

4. External model integration

SQL Server 2025 introduces EXTERNAL MODEL, which lets you register AI models (Azure OpenAI, OpenAI, Ollama, etc.) as first-class T-SQL objects and call them directly from queries. PostgreSQL doesn't have this built into the core engine — you typically generate embeddings outside the DB (Python, LangChain, application layer) and then insert the resulting vectors. Azure Database for PostgreSQL does offer an azure_ai extension that bridges this gap for Azure-hosted instances.

5. Which one fits your use case?

If your workload is already on SQL Server and you want everything — embeddings, vector search, and model calls — inside the engine with zero extra infrastructure, SQL Server 2025 is compelling. The DiskANN index is designed to scale to billions of vectors without offloading to a dedicated vector store.

If you're on PostgreSQL, pgvector gets you 90% of the way there with far less licensing cost and broader managed service support. HNSW indexes are mature and production-proven. The trade-off is that embedding generation stays outside the database — you own that pipeline.

Bottom line

Both platforms now let you store and query embeddings directly alongside relational data — which is the real win, because it eliminates the data synchronization headache of maintaining a separate vector store. SQL Server 2025 bets on deeper engine integration and managed model calls; PostgreSQL bets on ecosystem flexibility and open-source momentum. The syntax is surprisingly similar. Pick the one you're already running, and start small: create one table with a VECTOR column, generate embeddings for a real dataset, and measure your query latency with and without an ANN index. That benchmark will tell you more than any comparison article.