# timescale/pgvectorscale

Postgres extension for vector search (DiskANN), complements pgvector for performance and scale. Postgres OSS licensed.

Repository: https://github.com/timescale/pgvectorscale
Canonical: https://ross.abutalabs.com/products/pgvectorscale
Language: Rust
License: PostgreSQL
License Family: other
Last push: 2026-08-13T15:09:16+00:00

## Health v2 (maintenance only)
Score: 75/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 97, release rhythm 43, longevity 82
- inputs: {"age_days": 1160, "days_push": 20, "days_rel": 302, "gap_med": 32, "n_releases_24m": 8}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3116, forks 152 (observed 2026-08-28T04:07:44.148096+00:00)

## What it is
pgvectorscale is a PostgreSQL extension written in Rust that complements pgvector with a StreamingDiskANN index, statistical binary quantization, and label-based filtered vector search. It delivers significantly higher performance and cost-efficient embedding search for AI applications on self-hosted PostgreSQL.

## Use cases
- speed up pgvector similarity search in postgres
- run vector search at scale without a dedicated vector database
- replace pinecone with self-hosted postgres embeddings
- filtered vector search with metadata labels
- store and query millions of embeddings cost-efficiently
- build RAG applications on postgres

## When to choose
- you already use PostgreSQL and pgvector but need lower latency and higher throughput
- you want to self-host vector search instead of paying for managed vector databases
- you need label-filtered approximate nearest neighbor search
- you prefer a Rust-based extension with active development

## When to avoid
- you need a database other than PostgreSQL
- your vector workload is small enough that plain pgvector suffices
- you cannot install custom Postgres extensions (e.g., some managed hosts)
- you need turnkey managed infrastructure without self-hosting

## Facets
- artifact type: library
- maturity: active
- function: vector-database, search-engine, database
- domain: databases, machine-learning, developer-tools
- platform: self-hosted, cloud, rust
- tags: postgres-extension, pgvector, diskann, vector-search, embeddings, ann, pgrx, quantization, search, retrieval-augmented-generation, linux, docker

## Member repositories
- timescale/pgvectorscale (main) score 75

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:44.148096+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:26:39.765877+00:00, confidence not recorded.
  - readme: https://github.com/timescale/pgvectorscale (fetched 2026-08-28T04:07:44.148096+00:00, sha 55d119ef503f)
- Data as of 2026-08-30T08:39:29.467469+00:00.