--- title: "postgresml vs pgvecto.rs" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/postgresml-postgresml-vs-tensorchord-pgvecto-rs" tools: ["postgresml-postgresml", "tensorchord-pgvecto-rs"] --- # postgresml vs pgvecto.rs Neutral, constraint-first comparison with live GitHub stats. | | [postgresml](/tools/postgresml-postgresml.md) | [pgvecto.rs](/tools/tensorchord-pgvecto-rs.md) | | --- | --- | --- | | Tagline | Postgres with GPUs for ML/AI apps | Scalable, Low-latency and Hybrid-enabled Vector Search in Postgres | | Stars | 6,808 | 2,177 | | Forks | 365 | 84 | | Open issues | 155 | 76 | | Language | Rust | Rust | | Adopt for | PostgresML is a PostgreSQL extension enabling in-database machine learning operations with GPU acceleration. It provides various ML algorithms, supports large language models and RAG pipelines, and offers vector search. | `pgvecto.rs` is a Postgres extension designed for vector similarity search, written in Rust and supporting up to 65535 dimensions. It introduces advanced features such as VBASE method for hybrid vector-relational queries | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | Vector Databases, Inference & Serving | Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [postgresml](/tools/postgresml-postgresml.md) | [pgvecto.rs](/tools/tensorchord-pgvecto-rs.md) | | --- | --- | --- | | Days since push | 371d | 496d | | Open issues (now) | 155 | 76 | | Full report | [trust report](/tools/postgresml-postgresml/trust.md) | [trust report](/tools/tensorchord-pgvecto-rs/trust.md) | **Typed relationship:** postgresml _(alternative)_ pgvecto.rs Both postgresml and pgvecto.rs are scalable vector search solutions for PostgreSQL, addressing similar use cases in ML/AI applications but with possibly different implementations or features. ## Decision facts: postgresml - **Requirements:** A PostgreSQL database with the open-source pgml extension installed is required. - **Adopt for:** PostgresML is a PostgreSQL extension enabling in-database machine learning operations with GPU acceleration. It provides various ML algorithms, supports large language models and RAG pipelines, and offers vector search. ## Decision facts: pgvecto.rs - **Adopt for:** `pgvecto.rs` is a Postgres extension designed for vector similarity search, written in Rust and supporting up to 65535 dimensions. It introduces advanced features such as VBASE method for hybrid vector-relational queries ## Choose when ### Choose postgresml if… - License: postgresml is MIT, pgvecto.rs is Apache-2.0. - Requirements: A PostgreSQL database with the open-source pgml extension installed is required.. - Both postgresml and pgvecto.rs are scalable vector search solutions for PostgreSQL, addressing similar use cases in ML/AI applications but with possibly different implementations or features. - Tags unique to postgresml: clustering, embeddings, ai, artificial-intelligence. - Also covers Inference & Serving. - Leverage PostgresML when you need to perform ML operations on large datasets while reducing the overhead of data transfer and ensuring data consistency. ### Choose pgvecto.rs if… - License: pgvecto.rs is Apache-2.0, postgresml is MIT. - Both postgresml and pgvecto.rs are scalable vector search solutions for PostgreSQL, addressing similar use cases in ML/AI applications but with possibly different implementations or features. - Tags unique to pgvecto.rs: postgresql, rust, vector-search. - - **When high dimensionality support is required**: Supports up to 65535 dimensions which can be crucial if the use case involves very dense or complex feature spaces ## When NOT to use postgresml - Avoid using PostgresML if your application cannot support the requirement for a Rust-compiled extension to be installed on a PostgreSQL database. - Do not use this tool if GPU resources are constrained or unavailable in your deployment environment, as its performance benefits largely depend on available GPU acceleration. ## When NOT to use pgvecto.rs - - **If you prioritize stability over cutting-edge features**: Developers have pointed to a newer implementation named VectorChord which is said to offer superior stability; consider migrating if long- ## Common questions ### What is the difference between postgresml and pgvecto.rs? postgresml: Postgres with GPUs for ML/AI apps. pgvecto.rs: Scalable, Low-latency and Hybrid-enabled Vector Search in Postgres. See the comparison table for live GitHub stats and shared categories. ### When should I choose postgresml over pgvecto.rs? Choose postgresml over pgvecto.rs when License: postgresml is MIT, pgvecto.rs is Apache-2.0; Requirements: A PostgreSQL database with the open-source pgml extension installed is required.; Both postgresml and pgvecto.rs are scalable vector search solutions for PostgreSQL, addressing similar use cases in ML/AI applications but with possibly different implementations or features; Tags unique to postgresml: clustering, embeddings, ai, artificial-intelligence; Also covers Inference & Serving; Leverage PostgresML when you need to perform ML operations on large datasets while reducing the overhead of data transfer and ensuring data consistency. ### When should I choose pgvecto.rs over postgresml? Choose pgvecto.rs over postgresml when License: pgvecto.rs is Apache-2.0, postgresml is MIT; Both postgresml and pgvecto.rs are scalable vector search solutions for PostgreSQL, addressing similar use cases in ML/AI applications but with possibly different implementations or features; Tags unique to pgvecto.rs: postgresql, rust, vector-search; - **When high dimensionality support is required**: Supports up to 65535 dimensions which can be crucial if the use case involves very dense or complex feature spaces. ### When should I avoid postgresml? Avoid using PostgresML if your application cannot support the requirement for a Rust-compiled extension to be installed on a PostgreSQL database. Do not use this tool if GPU resources are constrained or unavailable in your deployment environment, as its performance benefits largely depend on available GPU acceleration. ### When should I avoid pgvecto.rs? - **If you prioritize stability over cutting-edge features**: Developers have pointed to a newer implementation named VectorChord which is said to offer superior stability; consider migrating if long- ### Is postgresml or pgvecto.rs more popular on GitHub? postgresml has more GitHub stars (6,808 vs 2,177). Stars measure visibility, not whether either tool fits your constraints. ### Are postgresml and pgvecto.rs open source? Yes - both are open-source projects on GitHub (postgresml: MIT, pgvecto.rs: Apache-2.0). ### Where can I find alternatives to postgresml or pgvecto.rs? GraphCanon lists graph-backed alternatives at /tools/postgresml-postgresml/alternatives and /tools/tensorchord-pgvecto-rs/alternatives (/tools/postgresml-postgresml/alternatives.md, /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 /compare/postgresml-postgresml-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, postgresml or pgvecto.rs? postgresml: Dormant. 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 postgresml and pgvecto.rs? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: postgresml: /tools/postgresml-postgresml/trust; pgvecto.rs: /tools/tensorchord-pgvecto-rs/trust. --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=postgresml-postgresml`](/api/graphcanon/graph?tool=postgresml-postgresml) - 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/_