Vector database comparisons online are written for teams with tens of millions of vectors and a dedicated infra budget. Most student and early-stage projects have under a million vectors and one free-tier Postgres instance.
At this scale, pgvector usually wins
If you already run Postgres, adding the pgvector extension avoids a second system to operate, a second bill, and a second set of credentials to secure. Index build time and query latency are fine up to roughly a million rows with an IVFFlat or HNSW index.
When a managed vector database earns its cost
- You need sub-50ms search at tens of millions of vectors with heavy concurrent traffic.
- You need built-in hybrid search or multi-tenant filtering you don't want to build yourself.
- Your team has no one comfortable operating Postgres extensions in production.
See choosing a vector database: FAISS, Qdrant, pgvector for the deeper comparison this piece assumes.
About Pranjul Rathour

Requirements gathering, on stage
Pranjul Rathour is a GenAI engineer from Kanpur, India, and CTO at SCULT INDIA, currently shipping production RAG,
fine-tuning and agentic AI systems, mentoring 200+ students through TechVerse Enclave, and judging and speaking at
student hackathons across India. Updated 2026-09-07.
Reach out if you want to talk GenAI, book a campus session, or invite him to judge:
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Pranjul Rathour · GenAI engineer, 3x hackathon winner, campus mentor. Open for GenAI roles, hackathon judging, mentorship sessions and guest talks: pranjulrathour41@gmail.com · Invite me to your campus
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