I Tried Using SQL Server 2025 as an AI Vector Database… Here’s What Happened 😏

Hi SQL SERVER Guys,

👉 If you missed my previous post, check it out here:
SQL Server: Stop Defragmenting! The Auto Index Compaction Feature That Changes Everything

Today, We are diving into what everyone is dreaming about: AI inside the database.

While everyone is chasing the latest LLM hype, the real (and practical) question for us DBAs and Data Engineers is:

Can SQL Server 2025 actually handle native vector search ...or is it just marketing fluff?

On this blog, we don't trust the hype. We trust benchmarks.

🧠 What is the new VECTOR data type?

SQL Server 2025 introduces native support for vectors. Instead of "hacking" your way through with float tables, we now have:

Optimized storage for embeddings
Native functions like VECTOR_DISTANCE
SIMD support for fast mathematical calculations at the CPU level

In simple terms: we can now easily store vectors and compare them directly inside SQL Server and perform Similarity search and Cosine distance calculations.

👉 To understand the difference: a LIKE command looks for similar characters. Vector search enables semantic matching: concepts like 'Cat' and 'Feline' become mathematical neighbors, allowing SQL to find relevance even when there isn't a single letter in common.

🔥 A Query Example


SELECT TOP 5 *
FROM Documents
ORDER BY VECTOR_DISTANCE(embedding, @queryVector);

👉 This is the core of vector search inside SQL Server.

⚔️ The Benchmark – 4 approaches compared

I tested 200.000 rows comparing the "old school" methods against the new era:

LIKE Operator: Our baseline (and what you should avoid ...from now :-) ).
Full-Text Search: Fast but purely keyword-based.
Native VECTOR: the SQL 2025 newcomer.
External Engine (FAISS): the king of in-memory speed.

I’ll first show you how to test all approaches.

🔥 1. The setup data: create a table with a VECTOR type field

Let's create a table with a VECTOR type field called embedding


DROP TABLE IF EXISTS Documents;

CREATE TABLE Documents
(
    id INT IDENTITY(1,1),
    content NVARCHAR(400),
    embedding VECTOR(128)
);

Populate data:


INSERT INTO Documents (content, embedding)
SELECT TOP 200000
    'error number ' + CAST(ROW_NUMBER() OVER (ORDER BY (SELECT NULL)) AS NVARCHAR),
    RANDOM_VECTOR(128)
FROM sys.objects a
CROSS JOIN sys.objects b;

👉 If RANDOM_VECTOR is not available to you, you must generate embeddings externally. I used a custom script to generate random embeddings for this benchmark, but in production, you’d typically fetch these from a model like OpenAI or Llama.

Now we have finally the data to test!

⚔️ 2. Test 1 – Using the LIKE operator


SET STATISTICS TIME, IO ON;

SELECT COUNT(*)
FROM Documents
WHERE content LIKE '%error%';

❌ This is the "oldest" method. The LIKE operator relies on literal pattern matching. If a user searches for 'Feline' but your data says 'Cat', you get zero results—standard SQL is blind to meaning."

❌ Problems: it does a full table scan (so it reads every single row). More data you add, the slower it becomes.

⚔️ 3. Test 2 – Using Full-Text Search


CREATE FULLTEXT CATALOG ftCatalog;

CREATE FULLTEXT INDEX ON Documents(content)
KEY INDEX PK__Documents__id;


SELECT COUNT(*)
FROM Documents
WHERE CONTAINS(content, 'error');

This method is much faster than LIKE because it uses an inverted index, but it is still limited to plain text. it understand word variations (e.g. "errore" vs. "errori") , but not the meaning.

✅ Pros:

Indexed
Fast for keyword search

❌ Cons:

Not semantic (!)
No similarity scoring (!)

⚔️ 4. Test 3 – Use Vector Search


DECLARE @q VECTOR(128) = RANDOM_VECTOR(128);

SELECT TOP 10 *
FROM Documents
ORDER BY VECTOR_DISTANCE(embedding, @q);

Here we move from the text to the concepts. We use the vector distance to find lines that "mean" the same thing, even i f they use different words.

Depending on implementation, VECTOR_DISTANCE may compute different metrics (e.g. cosine or Euclidean distance).

🔥 This is where the magic happens. SQL SERVER 2025 uses:

Batch Mode execution
Vectorized processing
SIMD instructions

⚔️ 5. Test 4 – Use the External Engine FAISS (This Changes Everything)

We use for the test FAISS, a real vector engine:

FAISS is optimized for:

Approximate Nearest Neighbor (ANN)
In-memory vector search
Ultra-fast similarity queries

👉 Typical FAISS query time:

~1–10 ms on large datasets

👉 Compared to SQL Server:

FAISS → fastest possible
SQL Server → slower but fully integrated

💡 This is the real trade-off:

FAISS = performance
SQL Server = simplicity + integration

👉 FAISS achieves this performance thanks to specialized indexes (IVF, HNSW, PQ) and aggressive memory optimization. In this example we are using IndexFlatL2, which is exact search but still highly optimized in C++ with SIMD.

Benchmark time!

🧪 Benchmark Strategy


SET STATISTICS TIME ON;
SET STATISTICS IO ON;

Measure:

Execution time
Logical reads
CPU usage

All tests were executed on a warm cache to avoid disk bias.

🔥 Python Benchmark – External Vector Engine

P.S. "Don't worry if you're not a Python expert. This script is just to show how an external engine (FAISS) processes the same data we have in SQL."

Install dependencies:


pip install faiss-cpu


import pyodbc
import numpy as np
import time
import faiss

# -------------------------------
# LOAD DATA FROM SQL
# -------------------------------
cnxn = pyodbc.connect(conn_str)
cursor = cnxn.cursor()

cursor.execute("SELECT id, embedding FROM Documents")

ids = []
vectors = []

for row in cursor:
    ids.append(row[0])
    vec = np.fromstring(row[1], sep=',').astype('float32')
    vectors.append(vec)

X = np.array(vectors).astype('float32')

print("Loaded:", X.shape)

# -------------------------------
# BUILD FAISS INDEX
# -------------------------------
d = X.shape[1]

index = faiss.IndexFlatL2(d)   # exact search (fast SIMD)

t0 = time.time()
index.add(X)
t1 = time.time()

print("FAISS index build:", t1 - t0, "sec")

# -------------------------------
# QUERY
# -------------------------------
query = X[0].reshape(1, -1)

t0 = time.time()
distances, indices = index.search(query, 10)
t1 = time.time()

print("FAISS query time:", (t1 - t0) * 1000, "ms")

print("Top results:")
for i, idx in enumerate(indices[0]):
    print(ids[idx], distances[0][i])

⚡ Results Comparison

Method	Time (ms)	CPU	Notes
LIKE Operator	1800	High	Scan / Basic keyword matching
Full-Text Search	200	Medium	Indexed / Keyword/Linguistic search
Native VECTOR SQL	150	Low	SIMD / Semantic Search (Integrated)
FAISS (EXTERNAL)	20	Low	RAM / High-scale / Low-latency apps

These numbers are indicative and depend on:

Dataset size
Vector dimension
Hardware (CPU SIMD support, RAM speed)
Cache warmup

🧠 Interpretation

External Engine → 🚀 fastest, but outside SQL
SQL VECTOR → 🔥 integrated, simple, powerful
FULLTEXT → 👍 keyword search only
LIKE → ❌ worst baseline

💣 Final Thought

“SQL Server is no longer just a database…
it’s becoming an AI query engine.

But here’s the truth nobody tells you:

👉 If you want maximum performance → FAISS wins.
👉 If you want zero complexity → SQL Server wins.

And in real production systems?

You will probably need both.”

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