timescale/pgvectorscale
Postgres extension for vector search (DiskANN), complements pgvector for performance and scale. Postgres OSS licensed.
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Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
1 release in the last 90 days, the latest 58 days ago, with 15 contributors on GitHub.
Gained 45% fewer stars in the last 90 days than in the 90 days before.
Use Cases & Benefits
- Implements a PostgreSQL extension for high-performance vector similarity search using a DiskANN-based index and advanced compression techniques.
- Delivers significantly lower latency and higher throughput for large-scale vector searches compared to alternatives, while reducing storage costs with efficient compression.
- Use for building scalable AI applications that require fast, approximate nearest neighbor search on large embedding datasets within PostgreSQL.
- Use for enabling label-filtered vector searches combining metadata filtering with similarity search for precise and efficient retrieval in categorized data.
- Use for integrating vector search capabilities into existing PostgreSQL environments with customizable index tuning and parallel index building for large datasets.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
PostgreSQL License
Permissive: commercial use, modification and redistribution are allowed with attribution.
Community files
- README: present
- Contributing guide: present
- Code of conduct: present
Watch-outs
No watch-outs from GStars' checks.
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From the README
pgvectorscale builds on pgvector with higher performance embedding search and cost-efficient storage for AI applications.
pgvectorscale complements pgvector, the open-source vector data extension for PostgreSQL, and introduces the following key innovations for pgvector data:
On a benchmark dataset of 50 million Cohere embeddings with 768 dimensions each, PostgreSQL with
pgvectorandpgvectorscaleachieves 28x lower p95 latency and 16x higher query throughput compared to Pinecone's storage optimized (s1) index for approximate nearest neighbor queries at 99% recall, all at 75% less cost when self-hosted on AWS EC2.To learn more about the performance impact of pgvectorscale, and details about benchmark methodology and results, see the pgvector vs Pinecone comparison blog post.
In contrast to pgvector, which is written in C, pgvectorscale is developed in Rust using the PGRX framework, offering the PostgreSQL community a new avenue for contributing to vector support.
Application developers or DBAs can use pgvectorscale with their PostgreSQL databases.
If you want to contribute to this extension, see how to build pgvectorscale from source in a developer environment and our testing guide.
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
Forks
160+14
Open issues
19-4
Compared with peers in Backend
Fastest-growing repositories GStars tracks in this cohort for the week of Nov 1, by 7-day star growth
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| anthropics/skills | 187.8K | +2.9K | 15 | — |
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| NationalSecurityAgency/ghidra | 98.2K | +2.3K | 64 | 14 days ago |
| microsoft/VibeVoice | 56.0K | +1.1K | 29 | — |
| timescale/pgvectorscale | 3.2K | — | 41 | 58 days ago |
| Category median | 56.0K | +2.3K | 29 | — |
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