SQL Server 2025 Vector Search for Hybrid RAG: A Benchmark

The Three Contenders

• SQL Server 2025 with VECTOR types and HNSW. Native vector column, cosine and L2 distances, hybrid queries that blend vector similarity with WHERE filters on any column.
• Azure AI Search. The dedicated search engine. Hybrid search with BM25, vector, and semantic ranker; deeply integrated with Azure OpenAI.
• pgvector on Azure Database for PostgreSQL. The open-source pattern for teams on Postgres. HNSW and IVFFlat indexes, straightforward SQL surface.

What "Hybrid" Actually Means

Hybrid RAG in 2026 typically combines three signals: vector similarity (for semantic match), keyword relevance (BM25 or equivalent for exact-term matching), and structured filters (tenant ID, date ranges, document type). The interesting engineering question is how cleanly each platform expresses all three in a single query.

SQL Server 2025. The Surprise Contender

-- SQL Server 2025: hybrid query with vector + structured filter in one statement
DECLARE @queryVec VECTOR(1536) = CAST(@embedding AS VECTOR(1536));

SELECT TOP 20
    d.id, d.title, d.content,
    VECTOR_DISTANCE('cosine', d.embedding, @queryVec) AS vecScore
FROM Documents d
WHERE d.tenantId = @tenantId
  AND d.publishedAt >= @since
  AND CONTAINS(d.content, @keywordQuery)           -- full-text
ORDER BY vecScore ASC;

-- HNSW index on the vector column is automatic after:
-- CREATE VECTOR INDEX vec_docs ON Documents(embedding) WITH (metric='cosine');

The underrated advantage here is that the vector, the keyword index, the tenant filter, and the published-date constraint all live in one query against one database. There is no dual-write problem, no eventual-consistency window, and no separate operational footprint. For teams already running SQL Server, this can be a dramatic simplification.

Azure AI Search. Still the Quality Leader

Azure AI Search remains the strongest at ranking quality. The semantic ranker, L2 re-ranking, and integrated query rewriting lift recall at the top of the result list in a way that is difficult to match with raw vector similarity. The tradeoff is a separate store to keep in sync with the transactional database, and a cost line item that scales with replica/partition count. For high-quality search where recall matters more than operational simplicity, AI Search still wins.

pgvector. The Pragmatic Choice on Postgres

pgvector has become the default for Postgres-based stacks. HNSW gives you competitive recall and latency; hybrid search is straightforward with tsvector for keyword signal. It is the closest Postgres analogue to the SQL Server 2025 story. Fewer moving parts, strong enough ranking for most B2B workloads.

Benchmark Shape

Indicative numbers on a 1M-document corpus, 1536-dimensional embeddings, typical hybrid query:

• p50 latency. SQL Server 2025 with HNSW: 18–35 ms. pgvector with HNSW: 15–30 ms. Azure AI Search: 25–60 ms depending on semantic ranker activation.
• Recall@10 on semantic queries. SQL Server 2025: competitive. pgvector: competitive. Azure AI Search with semantic ranker: leading.
• Recall@10 on keyword-heavy queries. All three are close when hybrid scoring is configured; AI Search still edges ahead on multi-lingual or Swedish-specific text.
• Indexing throughput. SQL Server 2025 and pgvector are comparable. AI Search handles streamed indexing more gracefully at high volumes.

When SQL Server 2025 Is the Right Call

• You are already on SQL Server, and a second data store is the single biggest source of operational complexity you want to avoid.
• Your RAG queries frequently join vector search results with transactional data (user permissions, tenant scoping, recent activity).
• Your corpus is moderate (millions, not billions) and ranking quality is adequate without a semantic ranker.

When Azure AI Search Still Wins

• Ranking quality is the product. You sell search, and a few percentage points of recall at rank 1–3 translate to user satisfaction.
• Multi-lingual search where the semantic ranker and cross-lingual embeddings carry meaningful weight.
• Integration with Azure AI studio / Foundry for RAG orchestration that expects AI Search as the retrieval backbone.

Recommendation

For most B2B internal-search workloads on SQL Server in 2026, keeping vectors in the primary database is the right call. You trade a small quality delta for a large reduction in operational surface. For consumer-facing or quality-critical search, AI Search remains worth the extra store. The worst choice is running both without a clear division of responsibility.