--- title: "cuvs vs pgvecto.rs" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/nvidia-cuvs-vs-tensorchord-pgvecto-rs" tools: ["nvidia-cuvs", "tensorchord-pgvecto-rs"] --- # cuvs vs pgvecto.rs *GraphCanon updated Jul 12, 2026* ## Verdict Pick cuvs if cuVS is a CUDA-based library for efficient GPU-accelerated vector search and clustering; pick pgvecto.rs if pgVector.rs is a Rust-based extension for PostgreSQL that introduces scalable vector search capabilities to facilitate efficient nearest-neighbor searches. It supports dimensions up to 65535 and multiple distance metrics. [cuvs](https://docs.rapids.ai/api/cuvs/stable/) reports 810 GitHub stars, 210 forks, and 645 open issues, last pushed Jul 11, 2026. [pgvecto.rs](https://docs.vectorchord.ai/getting-started/overview.html) has 2.2k stars, 84 forks, and 76 open issues, last pushed Feb 26, 2025. Figures are from public GitHub metadata via [cuvs's repository](https://github.com/NVIDIA/cuvs) and [pgvecto.rs's repository](https://github.com/tensorchord/pgvecto.rs). | | [cuvs](/tools/nvidia-cuvs.md) | [pgvecto.rs](/tools/tensorchord-pgvecto-rs.md) | | --- | --- | --- | | Tagline | A library for vector search and clustering on the GPU | Scalable, Low-latency and Hybrid-enabled Vector Search in Postgres. Revolutionize Vector Search, not Database. | | Stars | 810 | 2,177 | | Forks | 210 | 84 | | Open issues | 645 | 76 | | Language | Cuda | Rust | | Adopt for | cuVS is a CUDA-based library for efficient GPU-accelerated vector search and clustering. | PgVector.rs is a Rust-based extension for PostgreSQL that introduces scalable vector search capabilities to facilitate efficient nearest-neighbor searches. It supports dimensions up to 65535 and multiple distance metrics | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | Vector Databases | LLM Frameworks, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [cuvs](/tools/nvidia-cuvs.md) | [pgvecto.rs](/tools/tensorchord-pgvecto-rs.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 499d | | Open issues (now) | 645 | 76 | | Full report | [trust report](/tools/nvidia-cuvs/trust.md) | [trust report](/tools/tensorchord-pgvecto-rs/trust.md) | ## Decision facts: cuvs - **Adopt for:** cuVS is a CUDA-based library for efficient GPU-accelerated vector search and clustering. ## Decision facts: pgvecto.rs - **Pricing:** freemium - $0 Free - Basic usage of pgvecto.rs is free under the Apache-2.0 license; additional paid tiers for enterprise features may vary based on PostgreSQL hosting costs and setup services (if required). - **Requirements:** Min 1 GB RAM; Requires Docker - **Adopt for:** PgVector.rs is a Rust-based extension for PostgreSQL that introduces scalable vector search capabilities to facilitate efficient nearest-neighbor searches. It supports dimensions up to 65535 and multiple distance metrics ## Choose when ### Choose cuvs if… - cuvs is primarily Cuda; pgvecto.rs is Rust. - Tags unique to cuvs: anns, clustering, cuda, gpu. - cuvs ships Docker support for self-hosted deployment. - - When you need high-performance vector operations leveraging the parallel processing power of GPUs, specifically with CUDA. ### Choose pgvecto.rs if… - pgvecto.rs is primarily Rust; cuvs is Cuda. - Pricing: $0 Free - Basic usage of pgvecto.rs is free under the Apache-2.0 license; additional paid tiers for enterprise features may vary based on PostgreSQL hosting costs and setup services (if required).. - Requirements: Min 1 GB RAM; Requires Docker. - Tags unique to pgvecto.rs: chatgpt, faiss, gpt, hacktoberfest. - Also covers LLM Frameworks. - When requiring integration with the PostgreSQL database, pgvecto.rs offers a seamless solution without the need for separate vector databases. ## When NOT to use cuvs - - For environments where GPU resources are limited or unavailable because cuVS heavily relies on CUDA's capabilities for performance gains. - - When you prioritize portability across different hardware, as cuVS being tied to CUDA means it may not be optimal on non-NVIDIA GPUs or CPU-only systems. ## When NOT to use pgvecto.rs - Avoid using pgvecto.rs if your project does not leverage the PostgreSQL ecosystem; switching database systems would be more cost-effective than integrating with PostgreSQL just for vector search. - It might not be suitable when requiring ultra-specialized vector operations, as dedicated vector databases may offer more tailored functionalities. ## Common questions ### What is the difference between cuvs and pgvecto.rs? cuvs: A library for vector search and clustering on the GPU. pgvecto.rs: Scalable, Low-latency and Hybrid-enabled Vector Search in Postgres. Revolutionize Vector Search, not Database.. See the comparison table for live GitHub stats and shared categories. ### When should I choose cuvs over pgvecto.rs? Choose cuvs over pgvecto.rs when cuvs is primarily Cuda; pgvecto.rs is Rust; Tags unique to cuvs: anns, clustering, cuda, gpu; cuvs ships Docker support for self-hosted deployment; - When you need high-performance vector operations leveraging the parallel processing power of GPUs, specifically with CUDA. ### When should I choose pgvecto.rs over cuvs? Choose pgvecto.rs over cuvs when pgvecto.rs is primarily Rust; cuvs is Cuda; Pricing: $0 Free - Basic usage of pgvecto.rs is free under the Apache-2.0 license; additional paid tiers for enterprise features may vary based on PostgreSQL hosting costs and setup services (if required).; Requirements: Min 1 GB RAM; Requires Docker; Tags unique to pgvecto.rs: chatgpt, faiss, gpt, hacktoberfest; Also covers LLM Frameworks; When requiring integration with the PostgreSQL database, pgvecto.rs offers a seamless solution without the need for separate vector databases. ### When should I avoid cuvs? - For environments where GPU resources are limited or unavailable because cuVS heavily relies on CUDA's capabilities for performance gains. - When you prioritize portability across different hardware, as cuVS being tied to CUDA means it may not be optimal on non-NVIDIA GPUs or CPU-only systems. ### When should I avoid pgvecto.rs? Avoid using pgvecto.rs if your project does not leverage the PostgreSQL ecosystem; switching database systems would be more cost-effective than integrating with PostgreSQL just for vector search. It might not be suitable when requiring ultra-specialized vector operations, as dedicated vector databases may offer more tailored functionalities. ### Is cuvs or pgvecto.rs more popular on GitHub? pgvecto.rs has more GitHub stars (2,177 vs 810). Stars measure visibility, not whether either tool fits your constraints. ### Are cuvs and pgvecto.rs open source? Yes - both are open-source projects on GitHub (cuvs: Apache-2.0, pgvecto.rs: Apache-2.0). ### Where can I find alternatives to cuvs or pgvecto.rs? GraphCanon lists graph-backed alternatives at [cuvs alternatives](/tools/nvidia-cuvs/alternatives) and [pgvecto.rs alternatives](/tools/tensorchord-pgvecto-rs/alternatives) ([cuvs markdown twin](/tools/nvidia-cuvs/alternatives.md), [pgvecto.rs markdown twin](/tools/tensorchord-pgvecto-rs/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/nvidia-cuvs-vs-tensorchord-pgvecto-rs.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, cuvs or pgvecto.rs? cuvs: Very active. pgvecto.rs: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for cuvs and pgvecto.rs? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [cuvs trust report](/tools/nvidia-cuvs/trust); [pgvecto.rs trust report](/tools/tensorchord-pgvecto-rs/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=nvidia-cuvs`](/api/graphcanon/graph?tool=nvidia-cuvs) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_