--- title: "feast vs automl-gs" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/feast-dev-feast-vs-minimaxir-automl-gs" tools: ["feast-dev-feast", "minimaxir-automl-gs"] --- # feast vs automl-gs *GraphCanon updated Aug 4, 2026* ## Verdict Pick feast if feast, an open-source feature store for AI/ML, facilitates efficient management and retrieval of features used in machine learning models; pick automl-gs if automl-gs: Python tool for automated machine-learning model creation from CSV data. [feast](https://feast.dev) reports 7.2k GitHub stars, 1.4k forks, and 390 open issues, last pushed Jul 31, 2026. [automl-gs](https://github.com/minimaxir/automl-gs) has 1.9k stars, 181 forks, and 28 open issues, last pushed Oct 22, 2019. Figures are from public GitHub metadata via [feast's repository](https://github.com/feast-dev/feast) and [automl-gs's repository](https://github.com/minimaxir/automl-gs). | | [feast](/tools/feast-dev-feast.md) | [automl-gs](/tools/minimaxir-automl-gs.md) | | --- | --- | --- | | Tagline | The Open Source Feature Store for AI/ML | Automatically generate machine-learning models and code with input CSV and target field | | Stars | 7,188 | 1,869 | | Forks | 1,392 | 181 | | Open issues | 390 | 28 | | Language | Python | Python | | Adopt for | Feast, an open-source feature store for AI/ML, facilitates efficient management and retrieval of features used in machine learning models. | automl-gs: Python tool for automated machine-learning model creation from CSV data | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | Data & Retrieval | Data & Retrieval, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [feast](/tools/feast-dev-feast.md) | [automl-gs](/tools/minimaxir-automl-gs.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 2d | 2477d | | Open issues (now) | 390 | 28 | | Owner type | Organization | User | | Full report | [trust report](/tools/feast-dev-feast/trust.md) | [trust report](/tools/minimaxir-automl-gs/trust.md) | ## Decision facts: feast - **Adopt for:** Feast, an open-source feature store for AI/ML, facilitates efficient management and retrieval of features used in machine learning models. ## Decision facts: automl-gs - **Adopt for:** automl-gs: Python tool for automated machine-learning model creation from CSV data ## Choose when ### Choose feast if… - License: feast is Apache-2.0, automl-gs is MIT. - Tags unique to feast: big-data, data-engineering, data-quality, data-science. - Use Feast when your project requires versioning of features to support experimentation and model evolution over time, as it allows you to seamlessly retrieve historical feature data. ### Choose automl-gs if… - License: automl-gs is MIT, feast is Apache-2.0. - Tags unique to automl-gs: automl, keras, python, tensorflow. - Also covers Model Training. - Need to rapidly prototype models with limited ML expertise ## When NOT to use feast - Avoid Feast in scenarios where your project needs are minimal, such as smaller datasets or simpler projects that do not require the overhead of feature versioning or management. - Do not use Feast if you prefer a more generalized data storage solution without specific features geared towards ML feature management. Competitors might be better for broader data manipulation tasks. ## When NOT to use automl-gs - Complex feature engineering or non-standard data inputs required - Sensitive about licensing of the generated code ## Common questions ### What is the difference between feast and automl-gs? feast: The Open Source Feature Store for AI/ML. automl-gs: Automatically generate machine-learning models and code with input CSV and target field. See the comparison table for live GitHub stats and shared categories. ### When should I choose feast over automl-gs? Choose feast over automl-gs when License: feast is Apache-2.0, automl-gs is MIT; Tags unique to feast: big-data, data-engineering, data-quality, data-science; Use Feast when your project requires versioning of features to support experimentation and model evolution over time, as it allows you to seamlessly retrieve historical feature data. ### When should I choose automl-gs over feast? Choose automl-gs over feast when License: automl-gs is MIT, feast is Apache-2.0; Tags unique to automl-gs: automl, keras, python, tensorflow; Also covers Model Training; Need to rapidly prototype models with limited ML expertise. ### When should I avoid feast? Avoid Feast in scenarios where your project needs are minimal, such as smaller datasets or simpler projects that do not require the overhead of feature versioning or management. Do not use Feast if you prefer a more generalized data storage solution without specific features geared towards ML feature management. Competitors might be better for broader data manipulation tasks. ### When should I avoid automl-gs? Complex feature engineering or non-standard data inputs required Sensitive about licensing of the generated code ### Is feast or automl-gs more popular on GitHub? feast has more GitHub stars (7,188 vs 1,869). Stars measure visibility, not whether either tool fits your constraints. ### Are feast and automl-gs open source? Yes - both are open-source projects on GitHub (feast: Apache-2.0, automl-gs: MIT). ### Where can I find alternatives to feast or automl-gs? GraphCanon lists graph-backed alternatives at [feast alternatives](/tools/feast-dev-feast/alternatives) and [automl-gs alternatives](/tools/minimaxir-automl-gs/alternatives) ([feast markdown twin](/tools/feast-dev-feast/alternatives.md), [automl-gs markdown twin](/tools/minimaxir-automl-gs/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/feast-dev-feast-vs-minimaxir-automl-gs.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, feast or automl-gs? feast: Very active. automl-gs: 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 feast and automl-gs? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [feast trust report](/tools/feast-dev-feast/trust); [automl-gs trust report](/tools/minimaxir-automl-gs/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=feast-dev-feast`](/api/graphcanon/graph?tool=feast-dev-feast) - 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/_