Data as of Jul 25, 2026 · Based on 2,718,867 AI responses across 10,525 prompts · See how Parse measures this
pgvectorgithub.compgvector is an open-source extension that adds vector similarity search capabilities to PostgreSQL. It supports exact and approximate nearest neighbor search with various distance functions and vector types.
The market map
Vector Database Platforms →Where AI ranks pgvector
+ 7 more markets
Tone of voice
47% of how AI describes pgvector reads positive.
Words AI uses
AI reaches for open-source · native · vector database when it describes pgvector.
Perceived strengths & weaknesses
AI praises pgvector for cost; it docks it on rank.
Rivals
Qdrant is the brand AI weighs against pgvector most, and it leads on local development prototyping.
Sources
medium.com shapes more of what AI says about pgvector than any other source, at 9.4% of its citations.
Excerpts where pgvector appeared in the AI's answer
pgvector (PostgreSQL extension): Cheapest overall if you already run a Postgres instance
pgvector (PostgreSQL): If your backend already runs on PostgreSQL (via Supabase, Neon , or a managed RDS instance), the incremental cost of pgvector is $0 .
Excerpts where pgvector appeared in the AI's answer
pgvector (PostgreSQL) : An extension that brings native vector similarity search directly into PostgreSQL .
pgvector is an open-source extension for PostgreSQL that adds vector data types and HNSW/IVFFlat indexing.
Excerpts where pgvector appeared in the AI's answer
pgvector (Best if You Already Run PostgreSQL): If your organization already manages a secure, on-premise PostgreSQL instance, adding the pgvector extension is often the most practical choice.
pgvector (PostgreSQL extension) — Best if you already run PostgreSQL securely on-premise and your dataset is under 50–100 million vectors.
Excerpts where pgvector appeared in the AI's answer
pgvector (PostgreSQL) is great if your data already lives in Postgres, but it does *not* have native sparse/BM25 support.
pgvector (The Pragmatic Postgres Choice): Does not have native sparse BM25 indexing built directly into the vector extension.
Excerpts where pgvector appeared in the AI's answer
pgvector (Best for SQL Teams): If you are already within a PostgreSQL ecosystem, pgvector allows you to use standard SQL for time-based filtering combined with vector similarity
Excerpts where pgvector appeared in the AI's answer
pgvector (PostgreSQL): This is an open-source extension for PostgreSQL that allows you to store vector data in columns and perform similarity searches directly using SQL.
PGvector: While it is an extension for PostgreSQL , it is often integrated so tightly that it feels native for users already in the PostgreSQL ecosystem, allowing vectors to be stored directly in columns.
Excerpts where pgvector appeared in the AI's answer
pgvector + pgvectorscale (The Postgres-Native Choice) : Best if your total dataset is under 100 million vectors
pgvector (Best for Relational Co-location ): If your scale is under roughly 100 million vectors and you already run PostgreSQL , adding the pgvector extension
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