--- title: "hopsworks vs awesome-AutoML" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/logicalclocks-hopsworks-vs-windmaple-awesome-automl" tools: ["logicalclocks-hopsworks", "windmaple-awesome-automl"] --- # hopsworks vs awesome-AutoML *GraphCanon updated Oct 4, 2026* ## Verdict Pick hopsworks if hopsworks, an ML platform with robust data management and model serving capabilities, supports multiple cloud environments like AWS, Azure, and GCP; pick awesome-AutoML if curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. [hopsworks](https://hopsworks.ai) reports 1.3k GitHub stars, 159 forks, and 16 open issues, last pushed Feb 10, 2025. [awesome-AutoML](https://github.com/windmaple/awesome-AutoML) has 944 stars, 157 forks, and 1 open issues, last pushed Mar 24, 2026. Figures are from public GitHub metadata via [hopsworks's repository](https://github.com/logicalclocks/hopsworks) and [awesome-AutoML's repository](https://github.com/windmaple/awesome-AutoML). | | [hopsworks](/tools/logicalclocks-hopsworks.md) | [awesome-AutoML](/tools/windmaple-awesome-automl.md) | | --- | --- | --- | | Tagline | Data-Intensive AI platform with Feature Store | Curating AutoML research and resources | | Stars | 1,307 | 944 | | Forks | 159 | 157 | | Open issues | 16 | 1 | | Language | Java | - | | Adopt for | Hopsworks, an ML platform with robust data management and model serving capabilities, supports multiple cloud environments like AWS, Azure, and GCP. | Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. | | Persona | - | - | | Runtime | - | - | | License | AGPL-3.0 | GPL-3.0 | | Categories | Evaluation & Observability, Inference & Serving, Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [hopsworks](/tools/logicalclocks-hopsworks.md) | [awesome-AutoML](/tools/windmaple-awesome-automl.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 595d | 194d | | Open issues (now) | 16 | 1 | | Stars delta | +5 (30d) | +3 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/logicalclocks-hopsworks/trust.md) | [trust report](/tools/windmaple-awesome-automl/trust.md) | ## Decision facts: hopsworks - **Adopt for:** Hopsworks, an ML platform with robust data management and model serving capabilities, supports multiple cloud environments like AWS, Azure, and GCP. ## Decision facts: awesome-AutoML - **Adopt for:** Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. ## Choose when ### Choose hopsworks if… - License: hopsworks is AGPL-3.0, awesome-AutoML is GPL-3.0. - Tags unique to hopsworks: aws, azure, feature-store, gcp. - Also covers Evaluation & Observability, Inference & Serving. - When project requirements include a comprehensive feature store for AI applications ### Choose awesome-AutoML if… - License: awesome-AutoML is GPL-3.0, hopsworks is AGPL-3.0. - Tags unique to awesome-AutoML: automl, hyperparameter-optimization, meta-learning, neural-architecture-search. - When seeking comprehensive resources on diverse AutoML topics from recent and impactful research. ## When NOT to use hopsworks - If developers prefer a tool requiring less computational resources to install - In scenarios where the preferred language is not Java and compatibility is an issue ## When NOT to use awesome-AutoML - If looking for direct implementation advice as the repository focuses more on linking to resources rather than providing specific how-to guides. - When requiring real-time or interactive AutoML features, since it's a curation hub rather than an application tool. ## Common questions ### What is the difference between hopsworks and awesome-AutoML? hopsworks: Data-Intensive AI platform with Feature Store. awesome-AutoML: Curating AutoML research and resources. See the comparison table for live GitHub stats and shared categories. ### When should I choose hopsworks over awesome-AutoML? Choose hopsworks over awesome-AutoML when License: hopsworks is AGPL-3.0, awesome-AutoML is GPL-3.0; Tags unique to hopsworks: aws, azure, feature-store, gcp; Also covers Evaluation & Observability, Inference & Serving; When project requirements include a comprehensive feature store for AI applications. ### When should I choose awesome-AutoML over hopsworks? Choose awesome-AutoML over hopsworks when License: awesome-AutoML is GPL-3.0, hopsworks is AGPL-3.0; Tags unique to awesome-AutoML: automl, hyperparameter-optimization, meta-learning, neural-architecture-search; When seeking comprehensive resources on diverse AutoML topics from recent and impactful research. ### When should I avoid hopsworks? If developers prefer a tool requiring less computational resources to install In scenarios where the preferred language is not Java and compatibility is an issue ### When should I avoid awesome-AutoML? If looking for direct implementation advice as the repository focuses more on linking to resources rather than providing specific how-to guides. When requiring real-time or interactive AutoML features, since it's a curation hub rather than an application tool. ### Is hopsworks or awesome-AutoML more popular on GitHub? hopsworks has more GitHub stars (1,307 vs 944). Stars measure visibility, not whether either tool fits your constraints. ### Are hopsworks and awesome-AutoML open source? Yes - both are open-source projects on GitHub (hopsworks: AGPL-3.0, awesome-AutoML: GPL-3.0). ### Where can I find alternatives to hopsworks or awesome-AutoML? GraphCanon lists graph-backed alternatives at [hopsworks alternatives](/tools/logicalclocks-hopsworks/alternatives) and [awesome-AutoML alternatives](/tools/windmaple-awesome-automl/alternatives) ([hopsworks markdown twin](/tools/logicalclocks-hopsworks/alternatives.md), [awesome-AutoML markdown twin](/tools/windmaple-awesome-automl/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/logicalclocks-hopsworks-vs-windmaple-awesome-automl.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, hopsworks or awesome-AutoML? hopsworks: Dormant. awesome-AutoML: Slowing. 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 hopsworks and awesome-AutoML? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [hopsworks trust report](/tools/logicalclocks-hopsworks/trust); [awesome-AutoML trust report](/tools/windmaple-awesome-automl/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=logicalclocks-hopsworks`](/api/graphcanon/graph?tool=logicalclocks-hopsworks) - 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/_