Checklist · Vector Search
Vector Search Launch Checklist for 2026
Ship your vector search product with confidence using this phase-gated launch checklist. Prioritized tasks, time estimates and owner accountability ensure your team hits milestones without surprises.
Phase 01
Foundation
- c1high2-3 days
Define goals and KPIs (Vector Search)
Define success: embedding latency under 100ms, semantic recall above 85%, and index scaling to 10M+ vectors.
- c2medium1 week
Identify target audience (Vector Search)
Talk to ML engineers and data scientists running RAG systems—understand embedding model choices, reranking trade-offs and integration friction.
- c3critical1 day
Audit current state (Vector Search)
Test your vector store against PostgreSQL pgvector, Pinecone and Weaviate on latency, cost and feature completeness.
Phase 02
Execution
- c4critical1 day
Prioritize high-impact tasks (Vector Search)
Prioritize: sub-100ms latency beats fancy features; shipping filters and hybrid search early compounds early wins.
- c5critical1 day
Assign owners and deadlines (Vector Search)
Assign search, indexing and documentation owners; lock in ship date to prevent scope creep on launch week.
- c6critical1 day
Set up tracking (Vector Search)
Build a public dashboard of your vector search benchmarks—help users understand what your store offers vs. alternatives.
Phase 03
Launch & Review
- c7high2-3 days
Ship and verify (Vector Search)
Launch to 5 beta users doing semantic search; measure end-to-end latency, embedding freshness and indexing performance.
- c8medium1 week
Measure against KPIs (Vector Search)
Track queries per second, average latency, and failed requests; iterate fast if p95 latency exceeds targets.
- c9critical1 day
Iterate on results (Vector Search)
Collect feedback on API ergonomics, filtering syntax and pricing; ship small fixes that improve developer experience.
Pro tips
- Tackle critical items first
- Review the checklist weekly
- Adapt phases to your vector search context