Vector Database Matchmaker

Vector Database Matchmaker: practical plan

Requirements first

Clarify embedding dimensions, update rate, peak QPS, latency target, filter selectivity, recall target, tenant count, data residency and recovery needs. Two million chunks alone is not enough to select a product.

Architecture options

Evaluation protocol

Build a representative query set with human relevance judgments. Compare recall@k, filtered recall, p95 latency, update freshness, tenant leakage tests, cost and restore time. Test deletes and backfills.

Isolation and operations

Enforce tenant filters server-side, test cross-tenant adversarial queries, encrypt data and define retention. Monitor index lag, memory, rebuild times and query failures.

Recommendation

Benchmark two finalists using production-like filters and update rates. Select based on measured retrieval quality and total operating cost; do not choose from a feature checklist alone.

Measurement and review

Record the baseline, source, date range, denominator and owner. Use one primary measure plus quality guardrails; compare like-for-like cohorts and disclose missing data. Treat estimates as scenarios, not observed outcomes.

Risks and controls

Check relevance, consent, disclosure, workload and downside before scaling. Keep an evidence trail, obtain human review for material commitments, and pause if trust, quality or compliance signals deteriorate.

Next steps

1. Confirm the goal, audience and available capacity.
2. Gather the minimum evidence for a first test.
3. Assign an owner, budget and review date.
4. Run a small pilot, record outcomes and decide whether to iterate, expand or stop.