--- title: "autokeras vs hub" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/keras-team-autokeras-vs-tensorflow-hub" tools: ["keras-team-autokeras", "tensorflow-hub"] --- # autokeras vs hub *GraphCanon updated Aug 22, 2026* ## Verdict Pick autokeras if autoKeras simplifies deep learning model design through automated neural architecture search and is compatible with Python 3.7+ and TensorFlow 2.8.0+; pick hub if hub is specifically tailored to Python developers who wish to incorporate transfer learning into their TensorFlow projects with pre-trained model components for applications such as image classification. [autokeras](http://autokeras.com/) reports 9.3k GitHub stars, 1.4k forks, and 161 open issues, last pushed Nov 25, 2025. [hub](https://tensorflow.org/hub) has 3.5k stars, 1.6k forks, and 6 open issues, last pushed Jan 17, 2025. Figures are from public GitHub metadata via [autokeras's repository](https://github.com/keras-team/autokeras) and [hub's repository](https://github.com/tensorflow/hub). | | [autokeras](/tools/keras-team-autokeras.md) | [hub](/tools/tensorflow-hub.md) | | --- | --- | --- | | Tagline | AutoML library for deep learning | A library for transfer learning by reusing parts of TensorFlow models. | | Stars | 9,328 | 3,523 | | Forks | 1,393 | 1,641 | | Open issues | 161 | 6 | | Language | Python | Python | | Adopt for | AutoKeras simplifies deep learning model design through automated neural architecture search and is compatible with Python 3.7+ and TensorFlow 2.8.0+. | hub is specifically tailored to Python developers who wish to incorporate transfer learning into their TensorFlow projects with pre-trained model components for applications such as image classification. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | hub is licensed under Apache-2.0, allowing for broad use in both open source and commercial projects. | | Categories | Developer Tools, Model Training | Data & Retrieval, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [autokeras](/tools/keras-team-autokeras.md) | [hub](/tools/tensorflow-hub.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 251d | 581d | | Open issues (now) | 161 | 6 | | Stars delta | Unknown | +1 (30d) | | Open issues delta | Unknown | -5 (30d) | | Full report | [trust report](/tools/keras-team-autokeras/trust.md) | [trust report](/tools/tensorflow-hub/trust.md) | ## Decision facts: autokeras - **Adopt for:** AutoKeras simplifies deep learning model design through automated neural architecture search and is compatible with Python 3.7+ and TensorFlow 2.8.0+. ## Decision facts: hub - **Pricing:** freemium - The core functionalities of hub are free to use with an open-source license; however, additional services or enterprise support might incur costs. - **Requirements:** Requires a Python environment and TensorFlow installation to operate. - **Adopt for:** hub is specifically tailored to Python developers who wish to incorporate transfer learning into their TensorFlow projects with pre-trained model components for applications such as image classification. - **License detail:** hub is licensed under Apache-2.0, allowing for broad use in both open source and commercial projects. ## Choose when ### Choose autokeras if… - Tags unique to autokeras: autodl, automl, deep-learning, keras. - Also covers Developer Tools. - When your project involves deep learning tasks requiring minimal manual intervention in designing models. ### Choose hub if… - Pricing: The core functionalities of hub are free to use with an open-source license; however, additional services or enterprise support might incur costs.. - Requirements: Requires a Python environment and TensorFlow installation to operate.. - Tags unique to hub: embeddings, image-classification, ml, python. - Also covers Data & Retrieval. - When you need to leverage existing TensorFlow models and integrate specific parts of them for tasks like embedding or image-classification without retraining the entire model from scratch. ## When NOT to use autokeras - When working with Python versions older than 3.7 or TensorFlow versions older than 2.8.0, as AutoKeras is not compatible. - If your project emphasizes transparent, understandable model architecture over automated generation without human oversight. ## When NOT to use hub - When working strictly with non-TensorFlow frameworks such as PyTorch or MXNet, as hub is built specifically for enhancing and reusing models within TensorFlow. - If your project requires a more generalized approach to machine-learning without reliance on pre-existing model components, focusing instead on training models from the ground up. ## Common questions ### What is the difference between autokeras and hub? autokeras: AutoML library for deep learning. hub: A library for transfer learning by reusing parts of TensorFlow models.. See the comparison table for live GitHub stats and shared categories. ### When should I choose autokeras over hub? Choose autokeras over hub when Tags unique to autokeras: autodl, automl, deep-learning, keras; Also covers Developer Tools; When your project involves deep learning tasks requiring minimal manual intervention in designing models. ### When should I choose hub over autokeras? Choose hub over autokeras when Pricing: The core functionalities of hub are free to use with an open-source license; however, additional services or enterprise support might incur costs.; Requirements: Requires a Python environment and TensorFlow installation to operate.; Tags unique to hub: embeddings, image-classification, ml, python; Also covers Data & Retrieval; When you need to leverage existing TensorFlow models and integrate specific parts of them for tasks like embedding or image-classification without retraining the entire model from scratch. ### When should I avoid autokeras? When working with Python versions older than 3.7 or TensorFlow versions older than 2.8.0, as AutoKeras is not compatible. If your project emphasizes transparent, understandable model architecture over automated generation without human oversight. ### When should I avoid hub? When working strictly with non-TensorFlow frameworks such as PyTorch or MXNet, as hub is built specifically for enhancing and reusing models within TensorFlow. If your project requires a more generalized approach to machine-learning without reliance on pre-existing model components, focusing instead on training models from the ground up. ### Is autokeras or hub more popular on GitHub? autokeras has more GitHub stars (9,328 vs 3,523). Stars measure visibility, not whether either tool fits your constraints. ### Are autokeras and hub open source? Yes - both are open-source projects on GitHub (autokeras: Apache-2.0, hub: Apache-2.0). ### Where can I find alternatives to autokeras or hub? GraphCanon lists graph-backed alternatives at [autokeras alternatives](/tools/keras-team-autokeras/alternatives) and [hub alternatives](/tools/tensorflow-hub/alternatives) ([autokeras markdown twin](/tools/keras-team-autokeras/alternatives.md), [hub markdown twin](/tools/tensorflow-hub/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/keras-team-autokeras-vs-tensorflow-hub.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, autokeras or hub? autokeras: Slowing. hub: 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 autokeras and hub? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [autokeras trust report](/tools/keras-team-autokeras/trust); [hub trust report](/tools/tensorflow-hub/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=keras-team-autokeras`](/api/graphcanon/graph?tool=keras-team-autokeras) - 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/_