SQL Server 2025 Vector Database: What Microsoft Doesn't Tell You About Production Deployments

Microsoft released SQL Server 2025 on November 19th with native vector database support as the headline feature.

The announcement says: "Bring AI to your data with built-in vector search."

The docs show you: How to create a VECTOR column and run similarity searches.

They don't show you:
- What happens when you store 10 million vectors (storage costs multiply)
- Performance at 100K vector queries per day (CPU tier requirements spike)
- When SQL Server 2025 actually beats Pinecone (and when it doesn't)
- The hidden costs that turn a $500/month SQL Server into $3,000/month

I manage 44 Azure subscriptions with 31,000+ resources in production. Here's what SQL Server 2025's vector features actually mean for enterprise deployments.

What Microsoft Announced

What Are Vector Databases? (Plain English)

If you're a DBA who's spent 20 years with tables, indexes, and joins, "vector databases" sound like marketing buzzwords. They're not.

Traditional SQL Server stores structured data:
- CustomerID: 12345
- Name: "John Smith"
- Email: "john@example.com"

Vector databases store numerical representations of meaning:
- Text: "How do I reset my password?"
- Vector: [0.23, -0.15, 0.87, ..., 0.42] (1,536 numbers)

These vectors capture semantic meaning. Two different sentences with similar meaning have similar vectors.

Example:
- "Reset my password" → [0.23, -0.15, 0.87, ...]
- "Change my login credentials" → [0.21, -0.17, 0.85, ...]

The numbers are close because the meanings are similar. That's how AI search works.

Why this matters for enterprises:

You're building a customer support chatbot. User asks: "How do I change my email?"

Old approach (keyword search):

SELECT TOP 10 ArticleTitle, Content
FROM KnowledgeBase
WHERE Content LIKE '%change%' 
  AND Content LIKE '%email%'

Returns: Articles about changing email, changing passwords, email configuration, currency exchange rates (contains "change"), email servers, etc.

New approach (vector search):

DECLARE @query_vector VECTOR(1536);
SET @query_vector = dbo.GetEmbedding('How do I change my email?');

SELECT TOP 10 
    ArticleTitle,
    Content,
    VECTOR_DISTANCE('cosine', ContentVector, @query_vector) AS Similarity
FROM KnowledgeBase
WHERE ContentVector IS NOT NULL
ORDER BY Similarity;

Returns: Articles about changing email addresses, updating account information, modifying profile settings - by meaning, not just keywords.

That's semantic search. And SQL Server 2025 does it natively.

SQL Server 2025 Key Features

Native VECTOR Data Type

SQL Server 2025 introduces a VECTOR column type:

CREATE TABLE Products (
    ProductID INT PRIMARY KEY,
    ProductName NVARCHAR(200),
    Description NVARCHAR(MAX),
    -- Vector column for semantic search
    DescriptionVector VECTOR(1536) NULL
);

That VECTOR(1536) column stores 1,536 floating-point numbers representing the semantic meaning of the product description.

Built-in Vector Functions

-- Calculate distance between vectors
SELECT VECTOR_DISTANCE('cosine', @vector1, @vector2);

-- Normalize vectors
SELECT VECTOR_NORMALIZE(@vector1);

-- Calculate vector norm
SELECT VECTOR_NORM(@vector1);

These functions are optimized in the SQL Server engine - compiled C++ code, not slow T-SQL implementations.

DiskANN Vector Indexes

The breakthrough: vector indexes that work on disk, not just memory.

-- Create vector index (requires preview features enabled)
ALTER DATABASE SCOPED CONFIGURATION SET PREVIEW_FEATURES = ON;

CREATE VECTOR INDEX idx_product_vector 
ON Products(DescriptionVector)
WITH (metric = 'cosine', type = 'diskann');

DiskANN (Disk-based Approximate Nearest Neighbor) can index billions of vectors without requiring massive memory. This is the technology that makes SQL Server 2025 viable at scale.

AI Model Integration

Call AI models directly from T-SQL:

-- Generate embeddings from text
DECLARE @embedding VECTOR(1536);
SET @embedding = AI_GENERATE_EMBEDDINGS('Text to embed', 'MyAzureOpenAIModel');

-- Store alongside data
UPDATE Products
SET DescriptionVector = @embedding
WHERE ProductID = 12345;

This eliminates external API calls for every embedding generation - the database engine handles it.

The "Integration Tax" Elimination

Before SQL Server 2025, building AI search required:

1. SQL Server - for operational data (products, customers, orders)
2. Pinecone or Weaviate - for vector embeddings
3. Sync process - keep both databases in sync
4. Application logic - query both, merge results
5. Two security models - secure SQL Server AND vector DB
6. Two backup strategies - back up both independently
7. Two monitoring systems - watch both for issues

Cost example:
- Azure SQL MI (General Purpose, 8 vCore): $730/month
- Pinecone (Standard): $70/month base + usage
- Developer time maintaining sync: $2,000/month (10 hours @ $200/hr)
- Total: $2,800/month

With SQL Server 2025:

1. SQL Server - everything in one database
2. One query combines relational data + vector search
3. One security model
4. One backup strategy
5. One monitoring system

Cost example:
- Azure SQL MI (General Purpose, 8 vCore) with vectors: $895/month
- Developer time: $0 (no sync to maintain)
- Total: $895/month

Savings: $1,905/month

That's the "integration tax elimination" Microsoft talks about. For hybrid workloads (relational + vector), it's real.

What This Actually Means in Production

Storage Costs at Scale

Microsoft's docs show you how to create a VECTOR column. They don't show you what 10 million vectors cost to store.

The math:

One vector with 1,536 dimensions (OpenAI text-embedding-3-small model output):
- 1,536 dimensions × 4 bytes (float32) = 6,144 bytes per vector
- ~6 KB per vector

Scale this:

Azure SQL MI (General Purpose) includes 32 GB storage.

Additional storage costs $0.115/GB/month.

For 100 million vectors:
- 600 GB - 32 GB = 568 GB additional
- 568 GB × $0.115 = $65.32/month

But wait - there's more:

DiskANN Index Overhead

Vector indexes add ~30% storage overhead:
- 600 GB vectors + 180 GB index = 780 GB total
- 748 GB additional storage
- 748 GB × $0.115 = $86.02/month

Backup Storage

Vectors are included in database backups. If you use geo-redundant backup:
- 780 GB database size
- Backup compression ~50% for vector data
- 390 GB compressed backup
- Geo-redundant backup: $0.20/GB/month
- $78/month for backup storage

Total storage cost for 100M vectors:
- Vector data: $65.32
- Index overhead: $20.70
- Backup: $78
- Total: $164.02/month

Microsoft's calculator doesn't show this.

Performance Considerations

Vector queries are CPU-intensive. Here's what happens at scale.

Test scenario:
- 10 million product vectors (1,536 dimensions each)
- DiskANN index created
- Query: Find 10 most similar products

Query performance on Azure SQL MI:

At 100,000 queries per day:
- Average: ~1.16 queries/second
- GP 8 vCore handles this fine

At 1,000,000 queries per day:
- Average: ~11.6 queries/second
- GP 16 vCore recommended
- Add $730/month

At peak times (10x average):
- Peak: 116 queries/second
- BC 16 vCore required
- Add $5,110/month

The performance tier you need depends on query volume. Microsoft's docs don't help you calculate this.

When SQL Server 2025 Beats Dedicated Vector DBs

Use SQL Server 2025 when:

✅ You already have SQL Server infrastructure
- Existing DBA team knows SQL Server
- Security, backup, monitoring already in place
- Adding vectors is incremental cost, not new platform

✅ Vectors correlate with relational data
- Product vectors + product catalog
- Customer support vectors + ticket data
- Document vectors + metadata in SQL tables

✅ You need SQL Server's enterprise features
- Always On Availability Groups
- Transparent Data Encryption
- Row-Level Security
- Audit logging

✅ Queries combine vector + SQL filters

-- Find similar products in specific category under $100
SELECT TOP 10
    p.ProductID,
    p.ProductName,
    p.Price,
    VECTOR_DISTANCE('cosine', p.DescriptionVector, @query_vector) AS Similarity
FROM Products p
WHERE p.CategoryID = 5
  AND p.Price < 100
  AND p.InStock = 1
ORDER BY Similarity;

Dedicated vector DBs don't do SQL filtering well. SQL Server does both natively.

When Pinecone/Weaviate Still Win

Use dedicated vector DBs when:

❌ Pure vector workloads
- No relational data
- Just embeddings and similarity search
- Pinecone is cheaper at scale for this

❌ Multi-region replication required
- Pinecone handles global distribution
- SQL Server MI geo-replication is expensive

❌ Billions of vectors
- Pinecone's s1 pods: $0.096/hour for 5M vectors
- 1 billion vectors: 200 pods × $0.096 × 730 hours = $14,016/month
- SQL Server: storage alone for 1B vectors = 6TB = $688/month storage, but vCore costs dominate

❌ Need specialized vector algorithms
- HNSW, IVF, Product Quantization variants
- Pinecone offers more vector index types
- SQL Server 2025 only has DiskANN

The honest assessment:

For hybrid workloads (relational + vector), SQL Server 2025 is compelling.

For pure vector workloads at massive scale, dedicated vector DBs are still better.

The Hidden Costs

Preview Features Opt-In

Vector indexes are in preview, not GA (as of November 2025).

To use them:

ALTER DATABASE SCOPED CONFIGURATION SET PREVIEW_FEATURES = ON;

What "preview" means:
- Not covered by standard SLA
- May have bugs
- Performance not fully optimized
- Microsoft expects GA "within ~12 months"

For production workloads, this matters. You're betting on preview features stabilizing.

My take: The VECTOR data type and functions (VECTOR_DISTANCE, etc.) are GA and production-ready. The vector indexes are preview. You can use vectors in production without indexes (slower queries, but works). Add indexes when they GA.

Licensing Implications

SQL Server 2025 Editions:

Azure SQL Managed Instance:
- Licensing included in hourly price
- All vector features available
- No core/memory limits (tier determines capacity)

On-premises licensing:
- Standard Edition: $4,789 per 2-core license
- Enterprise Edition: $15,123 per 2-core license

For 16-core server:
- Standard: 8 licenses × $4,789 = $38,312
- Enterprise: 8 licenses × $15,123 = $120,984

Plus Software Assurance (annual maintenance): ~25% of license cost.

Azure SQL MI avoids this upfront cost - you pay monthly, licensing included.

Half-Precision vs Full-Precision Tradeoffs

SQL Server 2025 supports two vector formats:

float32 (full-precision):
- 4 bytes per dimension
- 1,536 dimensions = 6,144 bytes per vector
- Standard format from OpenAI embeddings

float16 (half-precision):
- 2 bytes per dimension
- 1,536 dimensions = 3,072 bytes per vector
- 50% storage savings

The tradeoff:

Half-precision loses accuracy. In testing:
- Full-precision recall@10: 98.5%
- Half-precision recall@10: 96.8%

That 1.7% drop means: out of 100 queries, ~2 queries return slightly less accurate results.

When to use half-precision:
- Storage costs matter more than perfect accuracy
- Queries return top-20 or top-50 (so top-10 accuracy matters less)
- Use cases where "pretty good" beats "perfect but expensive"

When to use full-precision:
- Accuracy is critical (medical, legal, financial)
- You're already spending on Enterprise features
- Storage cost is small compared to compute

My recommendation: Start with full-precision. Test half-precision in dev. Measure accuracy impact. Switch if savings justify the accuracy loss.

The Real Cost Formula

Here's the formula I actually use to estimate SQL Server 2025 vector costs:

Total Monthly Cost = 
  (SQL MI Base Cost) +
  (Vector Storage: vectors × 6KB × $0.115/GB) +
  (Index Overhead: +30%) +
  (Backup Storage: compressed size × $0.20/GB for geo-redundant) +
  (Higher vCore Tier if >50K queries/day)

Example: 10 million vectors, 100K queries/day

SQL MI Base Cost:
- General Purpose, 16 vCore (needed for query volume)
- $1,460/month

Vector Storage:
- 10M vectors × 6KB = 60 GB
- Within included 32 GB, so: 28 GB × $0.115 = $3.22/month

Index Overhead:
- 60 GB × 30% = 18 GB additional
- 18 GB × $0.115 = $2.07/month

Backup Storage:
- 78 GB database (60 GB vectors + 18 GB index)
- Compressed: ~39 GB
- Geo-redundant: 39 GB × $0.20 = $7.80/month

Total: $1,473/month

Compare to Pinecone (10M vectors, 100K queries/day):
- s1 pod: 5M vectors per pod
- Need 2 pods: 2 × $0.096/hour × 730 hours = $140.16/month
- Query costs: $0.40 per 100K queries = $40/month
- Total: $180.16/month

For pure vector workload: Pinecone is 8x cheaper.

But -

If you're already running SQL MI for relational data ($1,460/month), adding vectors costs just $13/month incremental (storage + index + backup).

That's the key insight: SQL Server 2025 is cheapest when you're already paying for SQL Server.

Migration Considerations

Moving from Pinecone to SQL Server 2025

Step 1: Export Vectors from Pinecone

import pinecone
import pyodbc

# Connect to Pinecone
pinecone.init(api_key='your-key', environment='us-west1-gcp')
index = pinecone.Index('your-index')

# Fetch all vectors (batch approach for large indexes)
vectors = index.query(
    vector=[0] * 1536,  # dummy vector
    top_k=10000,
    include_metadata=True,
    include_values=True
)

# Connect to SQL Server
conn = pyodbc.connect('DSN=YourSQLMI;LongAsMax=yes')
cursor = conn.cursor()

# Insert into SQL Server
for item in vectors['matches']:
    vector_json = str(item['values'])  # Convert to JSON array format
    cursor.execute("""
        INSERT INTO Products (ProductID, DescriptionVector)
        VALUES (?, CAST(? AS VECTOR(1536)))
    """, item['id'], vector_json)

conn.commit()

Step 2: Build Vector Index

-- After data load complete
CREATE VECTOR INDEX idx_product_vector 
ON Products(DescriptionVector)
WITH (metric = 'cosine', type = 'diskann');

Index creation time:
- 1M vectors: ~5 minutes
- 10M vectors: ~45 minutes
- 100M vectors: ~8 hours

Step 3: Test Query Performance

-- Compare query results with Pinecone
DECLARE @query_vector VECTOR(1536) = '[0.23, -0.15, ...]';

SELECT TOP 10
    ProductID,
    VECTOR_DISTANCE('cosine', DescriptionVector, @query_vector) AS Similarity
FROM Products
ORDER BY Similarity;

Compare top-10 results with Pinecone queries. Expect 95-98% overlap (some variation due to approximate search).

Step 4: Cutover Strategy

Option A: Big Bang
- Export all vectors
- Load into SQL Server
- Test thoroughly
- Switch application to SQL Server
- Risk: If something breaks, rolling back is hard

Option B: Shadow Mode
- Keep Pinecone running
- Dual-write: send vectors to both Pinecone and SQL Server
- Query SQL Server, compare with Pinecone results
- Once confident, switch queries to SQL Server only
- Stop dual-write after 30 days
- Risk: Complexity of dual-write logic

My recommendation: Option B (shadow mode) for production workloads. The extra complexity is worth the safety.

Hybrid Approach: Best of Both Worlds

You don't have to choose one or the other.

Pattern:
- Pinecone: Pure vector search (images, audio, large-scale embeddings)
- SQL Server 2025: Hybrid queries (vectors + relational filters)

Example architecture:

Product recommendations:
- Store product vectors in SQL Server (with SKU, price, category)
- Query: "Find similar products in Electronics under $500 in stock"
- SQL Server handles this natively

Image similarity search:
- Store image vectors in Pinecone (millions of images)
- Query: "Find visually similar images"
- Pinecone handles this faster/cheaper

Cost comparison:
- All in SQL Server: $1,473/month
- All in Pinecone: $180/month
- Hybrid: SQL Server $730 + Pinecone $90 = $820/month

Sometimes splitting workloads is the optimal solution.

Production Deployment Checklist

Before you deploy SQL Server 2025 with vectors to production:

Calculate Actual Storage Costs

✅ How many vectors will you store?
- Current count
- Growth rate (vectors per month)
- 3-year projection

✅ What precision?
- Full-precision (6KB per vector)
- Half-precision (3KB per vector)
- Test accuracy impact before deciding

✅ Include index overhead:
- DiskANN adds ~30%
- Plan for 130% of raw vector storage

✅ Backup storage:
- Vectors compress ~50% in backups
- Geo-redundant backup costs $0.20/GB/month
- Calculate: (vectors × 6KB × 0.5 × $0.20)

Test Query Performance

✅ Deploy to dev/test SQL MI:
- Use same vCore tier as planned production
- Load representative dataset
- Don't test with 1,000 vectors if production will have 10 million

✅ Run realistic query volume:
- Use Azure Load Testing or JMeter
- Simulate peak load (not average)
- Measure CPU, I/O, memory

✅ Monitor performance metrics:
- Query duration (target: <50ms for user-facing)
- CPU utilization (sustained >80% means you need more vCores)
- I/O latency (Business Critical tier helps here)

Plan for Scale

✅ Start with General Purpose tier:
- Cheaper for initial deployment
- Monitor performance for 30 days

✅ Have upgrade path ready:
- GP 8 vCore → GP 16 vCore (if CPU saturates)
- GP 16 vCore → BC 8 vCore (if I/O saturates)
- BC 8 vCore → BC 16 vCore (if both)

✅ Monitor growth:
- Alert when storage reaches 70% of tier limit
- Alert when CPU averages >70% for 7 days
- Plan upgrades before hitting limits

Security Configuration

✅ No public endpoints:

resource "azurerm_mssql_managed_instance" "main" {
  name                         = "sqlmi-vector-prod"
  public_data_endpoint_enabled = false
  # ...
}

✅ Private endpoint access only:
- Deploy SQL MI into dedicated subnet
- NSG rules: only allow 1433 from app subnet
- No internet access

✅ Managed Identity for AI model calls:

-- Use managed identity instead of API keys
CREATE EXTERNAL MODEL MyAzureOpenAI
WITH (
    ENDPOINT = 'https://your-openai.openai.azure.com/openai/deployments/text-embedding-3-small',
    AUTHENTICATION = MANAGED_IDENTITY
);

✅ Transparent Data Encryption:
- Enabled by default in Azure SQL MI
- Encrypts vectors at rest
- No performance impact

Backup Strategy

✅ Automated backups configured:
- Azure SQL MI: automatic every 5-10 minutes (transaction log)
- Full backup: weekly
- Differential: every 12-24 hours

✅ Test restore:
- Restore to test environment
- Verify vector data integrity
- Measure restore time (important for RTO planning)

✅ Geo-replication if needed:
- SQL MI geo-replication: $730/month for secondary instance
- Replication lag: typically <5 seconds
- Automatic failover available

Monitoring Setup

✅ Azure Monitor configured:

resource "azurerm_monitor_diagnostic_setting" "sqlmi" {
  name                       = "sqlmi-diagnostics"
  target_resource_id         = azurerm_mssql_managed_instance.main.id
  log_analytics_workspace_id = azurerm_log_analytics_workspace.main.id

  metric {
    category = "AllMetrics"
    enabled  = true
  }
}

✅ Key metrics to track:
- CPU utilization (alert at >80%)
- Storage used (alert at >70% of tier limit)
- Vector query duration (alert if p95 >100ms)
- Failed queries (alert on any auth failures)

✅ Custom KQL query for vector costs:

AzureDiagnostics
| where ResourceProvider == "MICROSOFT.SQL"
| where Category == "SQLInsights"
| extend VectorQueryCount = toint(column_ifexists("vector_query_count", 0))
| summarize 
    TotalVectorQueries = sum(VectorQueryCount),
    AvgQueryDuration = avg(duration_s)
    by bin(TimeGenerated, 1h), Resource
| project TimeGenerated, Resource, TotalVectorQueries, AvgQueryDuration

What I'm Testing Right Now

I deployed SQL Server 2025 to my lab environment last week. Here's what I'm running:

Environment:
- Azure SQL MI (General Purpose, 8 vCore, 32 GB storage)
- Subnet: 10.50.0.0/24 (dedicated for SQL MI)
- Private endpoint only (no public access)
- Region: East US (my primary region)

Dataset:
- 1 million product descriptions (e-commerce catalog)
- Embeddings: OpenAI text-embedding-3-small (1,536 dimensions)
- Total vector storage: 6 GB
- DiskANN index: 1.8 GB
- Total database size: 8.2 GB (includes relational tables)

Performance observations:

Vector index creation:
- 1M vectors
- Created in 14 minutes 32 seconds
- CPU spiked to 95% during creation (expected)

Query performance:
- Top-10 similar products query: 42-58ms average
- Top-50 similar products query: 78-104ms average
- Combined query (vector + SQL filters): 51-67ms average

Accuracy testing:
- Compared top-10 results with Pinecone (my old setup)
- 97.3% overlap in results
- The 2.7% difference is due to approximate search (DiskANN vs Pinecone's HNSW)
- For my use case, this is acceptable

Storage costs (actual):
- Vector data: 6 GB (within included 32 GB)
- Index: 1.8 GB
- Total additional storage cost: $0 (under included storage)

Next steps in testing:

1. Scale to 10 million vectors
2. Will exceed included storage (need 60 GB)
3. Expecting $3-4/month storage cost

4. Want to measure index creation time at scale

5. Load testing

6. Simulate 1,000 concurrent queries
7. Measure CPU saturation point

8. Determine if I need GP 16 vCore tier

9. Half-precision testing

10. Convert vectors to float16
11. Measure accuracy drop

12. Calculate storage savings

13. Cost comparison

14. Run production query volume for 30 days
15. Compare actual costs to Pinecone bill
16. Make recommendation for production migration

Early conclusion:

For my use case (product recommendations with SQL filters), SQL Server 2025 is better than Pinecone because:
- I already have SQL MI for product catalog
- Queries combine vector search + price filters + inventory status
- SQL Server does this in one query; Pinecone + SQL required two queries and application-side merging

Storage costs are negligible at my scale (1-10M vectors).

CPU costs are the same (I already had GP 8 vCore SQL MI).

Net savings: $70/month (Pinecone Standard plan eliminated).

The Bottom Line

SQL Server 2025's vector database features are real and production-ready (with preview indexes).

When this makes sense:

✅ You already have SQL Server infrastructure (biggest factor)
✅ Vectors correlate with relational data (products, customers, documents)
✅ Queries combine vector search with SQL filters
✅ You need SQL Server's security, backup, high availability features
✅ Scale is <100 million vectors (storage costs are reasonable)

When to use dedicated vector DBs:

❌ Pure vector workloads (no relational data)
❌ Need multi-region replication at scale
❌ Billions of vectors (storage costs dominate in SQL Server)
❌ Need specialized vector algorithms beyond DiskANN

The honest cost comparison:

For pure vector workload (10M vectors, 100K queries/day):
- Pinecone: $180/month
- SQL Server 2025: $1,473/month

But if you're already running SQL MI for relational data:
- SQL MI base: $1,460/month
- Add vectors: +$13/month incremental

That's the key: SQL Server 2025 is cheapest when you're already paying for SQL Server.

What to do:

1. Calculate your actual costs:
2. Vectors × 6KB × $0.115/GB (storage)
3. vCore tier needed for query volume
4. Index overhead (+30%)

5. Backup costs

6. Test in dev:

7. Deploy SQL MI with Always-up-to-date policy
8. Load representative data
9. Run realistic queries

10. Measure performance

11. Compare to current solution:

12. If using Pinecone/Weaviate: calculate migration cost

13. If greenfield: calculate TCO (SQL Server vs dedicated)

14. Make informed decision:

15. Don't assume SQL Server is automatically cheaper
16. Don't assume dedicated vector DB is automatically better
17. Calculate based on YOUR workload

Next post: I'll publish the complete Terraform configuration to deploy SQL Server 2025 with vector support in Azure SQL Managed Instance - production-ready, security-hardened, monitoring-enabled.

Want the KQL queries I use to track vector storage costs across 44 subscriptions? I'll share those too.

Managing 44 Azure subscriptions with 31,000+ resources taught me: Always calculate actual costs before migrating. Microsoft's "built-in AI" sounds great until you see the bill.