Data as of Sep 18, 2026 · Based on 3,315,446 AI responses across 10,525 prompts · See how Parse measures this
11 of 18 measured questions
pgvectorgithub.compgvector is an open-source PostgreSQL extension that adds native vector storage and similarity search to Postgres. It supports exact and approximate nearest-neighbor search, with distance metrics such as L2, inner product, cosine, L1, and supports multiple vector types (single-precision, half-precision, binary, and sparse). The extension integrates with any language via a PostgreSQL client, is ACID compliant, and can be installed through Docker, Homebrew, PGXN, APT/YUM, or conda-forge, enabling users to create vector columns and query nearest neighbors directly in SQL.
The market map · 5 of 42 labelled
Vector Database Platforms →42%positive
excellentopen-sourcenativecheapestbestsimplevector databaseideal
Strengths
Weaknesses
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 Extension) — The best choice if you already run Postgres in production.
pgvector : An extension for PostgreSQL that turns your existing relational database into a vector store, ideal if you already manage on-prem Postgres infrastructure.
Excerpts where pgvector appeared in the AI's answer
pgvector (PostgreSQL Extension) - Best for: Developers already using PostgreSQL who want to avoid adding a brand-new database to their stack.
pgvector (PostgreSQL Extension) - Best for: Relational apps where you already use PostgreSQL and want to keep your stack unified.
Excerpts where pgvector appeared in the AI's answer
pgvector (PostgreSQL) : The go-to choice if you want to keep your vector data right alongside your primary relational data .
pgvector (PostgreSQL) : The ideal choice if you already rely on Postgres. By adding the pgvector extension, you can store and query vector embeddings natively alongside relational data using standard SQL.
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 (+ PostgreSQL): Native SQL capability. Because it lives inside PostgreSQL, you can combine vector similarity search directly with complex SQL timestamp math
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 (with pgvectorscale): Best for scale under 100 million vectors within PostgreSQL
pgvector + pgvectorscale (PostgreSQL): Best if your existing data lives in Postgres.
Excerpts where pgvector appeared in the AI's answer
pgvector + pgvectorscale: Best if under 50M-100M vectors and you want to stick with PostgreSQL . If you are experiencing lock contention or slow queries on standard Postgres, adding pgvectorscale introduces streaming disk ANN indexes that radically boost QPS
pgvector (with pgvectorscale): If you are already in the Postgres ecosystem, recent 2026 benchmarks show pgvectorscale can compete with specialized systems
