Choosing a Vector Database — pgvector vs Pinecone vs Qdrant
Start with Postgres
Under ~1M vectors with metadata filtering, pgvector inside your existing Postgres wins on simplicity — one backup, one transaction model, zero new infra. Most RAG projects never outgrow it. Premature vector-DB adoption is premature scaling.
When to go dedicated
Cross 5–10M vectors, need hybrid search tuning, or require multi-tenant performance isolation — then evaluate Qdrant (self-host control) or Pinecone (managed simplicity). Benchmark recall@k on your embeddings, not marketing numbers.
Filtering matters more than distance
Production retrieval is 70% metadata filtering (tenant, date, permissions) and 30% similarity. Whatever you choose must filter fast at scale — test filtered queries specifically, since unfiltered benchmarks mislead.
Ops checklist
Backup/restore tested, index rebuild time measured, embedding-version migration planned. Vectors are derived data: you must be able to rebuild the entire index from source documents within your RTO.
Related Articles
AI Guardrails for Production — PII, Jailbreaks, and Human Review
Layered defenses for LLM apps: input filters, output checks, and escalation paths that actually work.
Document Extraction Pipelines — From PDFs to Structured Data
OCR, layout-aware chunking, and validation loops for invoices, resumes, and lab reports.
Controlling LLM Costs — Caching, Routing, and Model Tiers
Cut inference spend 60–90% with semantic caching, small-model routing, and aggressive prompt hygiene.
Written by AI Engineer
Specialized engineering teams at Omnetra focus on writing high-performance code, ensuring API security, and optimizing layouts for client success.