--- title: "deeplake vs postgresml" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/activeloopai-deeplake-vs-postgresml-postgresml" tools: ["activeloopai-deeplake", "postgresml-postgresml"] --- # deeplake vs postgresml *GraphCanon updated Sep 30, 2026* ## Verdict Pick deeplake if deeplake is an AI Data Runtime for Agents that integrates a serverless PostgreSQL with a multimodal data lake, designed for scalable retrieval and training; pick postgresml if postgresML offers PostgreSQL with GPU acceleration for ML, distinguishing by its support of embedding operations directly into SQL queries. [deeplake](https://deeplake.ai) reports 9.2k GitHub stars, 722 forks, and 67 open issues, last pushed May 21, 2026. [postgresml](https://postgresml.org) has 6.8k stars, 365 forks, and 149 open issues, last pushed Jul 1, 2025. Figures are from public GitHub metadata via [deeplake's repository](https://github.com/activeloopai/deeplake) and [postgresml's repository](https://github.com/postgresml/postgresml). | | [deeplake](/tools/activeloopai-deeplake.md) | [postgresml](/tools/postgresml-postgresml.md) | | --- | --- | --- | | Tagline | AI Data Runtime for Agents | Postgres with GPUs for ML/AI apps | | Stars | 9,239 | 6,817 | | Forks | 722 | 365 | | Open issues | 67 | 149 | | Language | C++ | Rust | | Adopt for | Deeplake is an AI Data Runtime for Agents that integrates a serverless PostgreSQL with a multimodal data lake, designed for scalable retrieval and training. | PostgresML offers PostgreSQL with GPU acceleration for ML, distinguishing by its support of embedding operations directly into SQL queries. | | Persona | - | - | | Runtime | - | - | | License | Deeplake is available under the Apache-2.0 license, allowing for free use, modification, and distribution. | MIT | | Categories | Data & Retrieval, Model Training, Vector Databases | Data & Retrieval, Model Training, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [deeplake](/tools/activeloopai-deeplake.md) | [postgresml](/tools/postgresml-postgresml.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 119d | 412d | | Open issues (now) | 67 | 149 | | Stars delta | +15 (30d) | +3 (30d) | | Open issues delta | +4 (30d) | 0 (30d) | | Full report | [trust report](/tools/activeloopai-deeplake/trust.md) | [trust report](/tools/postgresml-postgresml/trust.md) | ## Decision facts: deeplake - **Pricing:** freemium - Deeplake offers a free tier with options for premium services, though specific pricing details are not provided in the repository. - **Requirements:** Min 4 GB RAM; Installation can be done via pip, making it accessible for Python environments. - **Adopt for:** Deeplake is an AI Data Runtime for Agents that integrates a serverless PostgreSQL with a multimodal data lake, designed for scalable retrieval and training. - **License detail:** Deeplake is available under the Apache-2.0 license, allowing for free use, modification, and distribution. ## Decision facts: postgresml - **Adopt for:** PostgresML offers PostgreSQL with GPU acceleration for ML, distinguishing by its support of embedding operations directly into SQL queries. ## Choose when ### Choose deeplake if… - deeplake is primarily C++; postgresml is Rust. - License: deeplake is Apache-2.0, postgresml is MIT. - Pricing: Deeplake offers a free tier with options for premium services, though specific pricing details are not provided in the repository.. - Requirements: Min 4 GB RAM; Installation can be done via pip, making it accessible for Python environments.. - Tags unique to deeplake: agent, agentic-rag, clawbot, computer-vision. - When you need a serverless PostgreSQL setup integrated with a multimodal data lake for efficient data retrieval and training. ### Choose postgresml if… - postgresml is primarily Rust; deeplake is C++. - License: postgresml is MIT, deeplake is Apache-2.0. - Tags unique to postgresml: ann, approximate-nearest-neighbor-search, classification, clustering. - Requires integration of machine learning tasks directly within PostgreSQL queries ## When NOT to use deeplake - If your project does not require serverless PostgreSQL or multimodal data handling capabilities. - When you are looking for a solution that does not support C++ as the primary language, as Deeplake is built with C++. - If your use case does not benefit from the specific integration of PostgreSQL with a data lake, and you prefer a more traditional database setup. ## When NOT to use postgresml - Teams not utilizing or unwilling to migrate to PostgreSQL databases - Applications where GPU access is limited, thus constraining ML capabilities ## Common questions ### What is the difference between deeplake and postgresml? deeplake: AI Data Runtime for Agents. postgresml: Postgres with GPUs for ML/AI apps. See the comparison table for live GitHub stats and shared categories. ### When should I choose deeplake over postgresml? Choose deeplake over postgresml when deeplake is primarily C++; postgresml is Rust; License: deeplake is Apache-2.0, postgresml is MIT; Pricing: Deeplake offers a free tier with options for premium services, though specific pricing details are not provided in the repository.; Requirements: Min 4 GB RAM; Installation can be done via pip, making it accessible for Python environments.; Tags unique to deeplake: agent, agentic-rag, clawbot, computer-vision; When you need a serverless PostgreSQL setup integrated with a multimodal data lake for efficient data retrieval and training. ### When should I choose postgresml over deeplake? Choose postgresml over deeplake when postgresml is primarily Rust; deeplake is C++; License: postgresml is MIT, deeplake is Apache-2.0; Tags unique to postgresml: ann, approximate-nearest-neighbor-search, classification, clustering; Requires integration of machine learning tasks directly within PostgreSQL queries. ### When should I avoid deeplake? If your project does not require serverless PostgreSQL or multimodal data handling capabilities. When you are looking for a solution that does not support C++ as the primary language, as Deeplake is built with C++. If your use case does not benefit from the specific integration of PostgreSQL with a data lake, and you prefer a more traditional database setup. ### When should I avoid postgresml? Teams not utilizing or unwilling to migrate to PostgreSQL databases Applications where GPU access is limited, thus constraining ML capabilities ### Is deeplake or postgresml more popular on GitHub? deeplake has more GitHub stars (9,239 vs 6,817). Stars measure visibility, not whether either tool fits your constraints. ### Are deeplake and postgresml open source? Yes - both are open-source projects on GitHub (deeplake: Apache-2.0, postgresml: MIT). ### Where can I find alternatives to deeplake or postgresml? GraphCanon lists graph-backed alternatives at [deeplake alternatives](/tools/activeloopai-deeplake/alternatives) and [postgresml alternatives](/tools/postgresml-postgresml/alternatives) ([deeplake markdown twin](/tools/activeloopai-deeplake/alternatives.md), [postgresml markdown twin](/tools/postgresml-postgresml/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/activeloopai-deeplake-vs-postgresml-postgresml.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, deeplake or postgresml? deeplake: Slowing. postgresml: 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 deeplake and postgresml? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [deeplake trust report](/tools/activeloopai-deeplake/trust); [postgresml trust report](/tools/postgresml-postgresml/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=activeloopai-deeplake`](/api/graphcanon/graph?tool=activeloopai-deeplake) - 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/_