Evaluate Native Vector Capabilities vs Dedicated Vector/Search Systems Using Workload Evidence

Learning outcomes

The final decision is not “SQL Server vectors versus a fashionable vector database.” It is whether a specific ServiceHub workload meets its quality, latency, throughput, update, security and operational objectives with acceptable complexity. SQL Server has a strong advantage when relational filtering, transactional data and vector ranking share one boundary. A specialized search/vector system can have advantages in independent scaling, indexing choices or search features. Benchmark the workload contract, not brand claims.

1. Define acceptance criteria before timing the query

A nearest-neighbor system is correct only relative to a quality target. Exact VECTOR_DISTANCE gives a reference ordering for the candidate set. Approximate search should be scored against that reference—for example recall@k—before comparing latency. Also record filter selectivity: searching 500 authorized candidates is a different workload from searching 50 million rows globally.

USE ServiceHubAILab;GOIF OBJECT_ID(N'lab23.VectorBenchmark', N'U') IS NULLBEGIN    CREATE TABLE lab23.VectorBenchmark    (        benchmark_id bigint IDENTITY PRIMARY KEY,        run_at datetime2(3) NOT NULL DEFAULT SYSUTCDATETIME(),        method varchar(30) NOT NULL,        query_label varchar(50) NOT NULL,        candidate_rows int NOT NULL,        top_k int NOT NULL,        elapsed_ms decimal(18,3) NULL,        recall_at_k decimal(9,6) NULL,        result_hash varbinary(32) NULL,        engine_build nvarchar(50) NOT NULL,        compatibility_level int NOT NULL,        preview_features bit NOT NULL,        note nvarchar(400) NULL    );END;

Elapsed time must be measured from the client or a controlled harness, not invented inside the lesson. Record warm/cold cache policy, concurrency, CPU/memory/container limits, candidate row count, query shape and build. Otherwise the number is not reproducible.

2. Exact SQL Server search becomes the quality oracle

USE ServiceHubAILab;GODECLARE @q vector(4)='[0.84,0.16,0.04,0.04]';DROP TABLE IF EXISTS #exact;SELECT TOP (5)    item_id,    VECTOR_DISTANCE('cosine', @q, embedding) AS distanceINTO #exactFROM lab23.KnowledgeItemWHERE is_active=1ORDER BY distance, item_id;SELECT * FROM #exact ORDER BY distance, item_id;SELECT HASHBYTES('SHA2_256',       STRING_AGG(CONVERT(varchar(20), item_id), ',') WITHIN GROUP (ORDER BY distance, item_id))       AS result_hashFROM #exact;

The result hash is useful regression evidence for this deterministic teaching dataset. In a real embedding/model upgrade you normally expect some result changes, so quality evaluation uses labeled relevance judgments rather than requiring identical hashes.

3. Optional approximate search: score quality before celebrating speed

If you enable current preview vector indexing in a development database, collect the approximate top-k item IDs and compare them with the exact set. Recall@k is the fraction of exact top-k items recovered by the approximate method. That is not the only quality metric, but it makes the trade explicit.

-- Example scoring harness; populate #approx from the method under test.-- #exact is the exact reference from the previous step.CREATE TABLE #approx(item_id int PRIMARY KEY);INSERT #approx(item_id)SELECT TOP (5) item_idFROM #exactORDER BY item_id; -- deterministic stand-in for the mandatory offline labDECLARE @k int = 5;SELECT    CAST(COUNT_BIG(*) * 1.0 / @k AS decimal(9,6)) AS recall_at_kFROM #approx AS aJOIN #exact AS e ON e.item_id = a.item_id;

For a real preview VECTOR_SEARCH run, replace the stand-in with the approximate result IDs and preserve the exact reference unchanged. Also measure p50/p95/p99 latency across repeated queries, throughput under representative concurrency, index build/maintenance time and memory/storage growth. One fast query is not a capacity plan.

4. Decision matrix: which boundary should own similarity search?

Neither column wins automatically. A dedicated system adds synchronization and security boundaries; SQL Server-native search adds CPU/storage competition with transactional work. The right architecture is the one that satisfies measured SLOs with an acceptable failure/operations model.

5. Close the chapter with a reversible decision and cleanup

USE ServiceHubAILab;GOSELECT    SERVERPROPERTY('ProductVersion') AS product_version,    d.compatibility_level,    cfg.value AS preview_featuresFROM sys.databases AS dCROSS JOIN sys.database_scoped_configurations AS cfgWHERE d.name = DB_NAME()  AND cfg.name = N'PREVIEW_FEATURES';GOUSE master;GOIF DB_ID(N'ServiceHubAILab') IS NOT NULLBEGIN    ALTER DATABASE ServiceHubAILab SET SINGLE_USER WITH ROLLBACK IMMEDIATE;    DROP DATABASE ServiceHubAILab;END;GO

Do not enable preview features in production just to make a benchmark look better. Keep the exact GA path as a fallback until the preview dependency has a deliberate acceptance/rollback plan. Chapter 24 moves directly into upgrades and compatibility change management—the same discipline required when SQL Server 2025 preview features evolve through cumulative updates.

Authoritative references

• Vector search and vector indexes
• CREATE VECTOR INDEX (preview)
• VECTOR_SEARCH (preview)
• VECTOR_DISTANCE
• Preview features FAQ
• SQL Server 2025 release notes