--- title: "archai vs hub" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/microsoft-archai-vs-tensorflow-hub" tools: ["microsoft-archai", "tensorflow-hub"] --- # archai vs hub *GraphCanon updated Jul 12, 2026* ## Verdict Pick archai when license: archai is MIT, hub is Apache-2.0; pick hub when license: hub is Apache-2.0, archai is MIT. [archai](https://microsoft.github.io/archai) reports 485 GitHub stars, 93 forks, and 4 open issues, last pushed Nov 24, 2025. [hub](https://tensorflow.org/hub) has 3.5k stars, 1.6k forks, and 14 open issues, last pushed Jan 17, 2025. Figures are from public GitHub metadata via [archai's repository](https://github.com/microsoft/archai) and [hub's repository](https://github.com/tensorflow/hub). | | [archai](/tools/microsoft-archai.md) | [hub](/tools/tensorflow-hub.md) | | --- | --- | --- | | Tagline | Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research. | A library for transfer learning by reusing parts of TensorFlow models. | | Stars | 485 | 3,521 | | Forks | 93 | 1,644 | | Open issues | 4 | 14 | | Language | Python | Python | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | Model Training | Model Training, Vector Databases, Computer Vision | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [archai](/tools/microsoft-archai.md) | [hub](/tools/tensorflow-hub.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 229d | 539d | | Open issues (now) | 4 | 14 | | Full report | [trust report](/tools/microsoft-archai/trust.md) | [trust report](/tools/tensorflow-hub/trust.md) | ## Choose when ### Choose archai if… - License: archai is MIT, hub is Apache-2.0. - Tags unique to archai: model-compression, automl, deep-learning, nas. - More recently updated (last pushed Nov 24, 2025). ### Choose hub if… - License: hub is Apache-2.0, archai is MIT. - Tags unique to hub: ml, embeddings, python, image-classification. - Also covers Vector Databases, Computer Vision. ## When NOT to use archai - Last GitHub push was 230 days ago (slowing maintenance, Nov 24, 2025). Validate activity before betting a new project on archai. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ## When NOT to use hub - Last GitHub push was 540 days ago (dormant maintenance, Jan 17, 2025). Validate activity before betting a new project on hub. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## Common questions ### What is the difference between archai and hub? archai: Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research.. 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 archai over hub? Choose archai over hub when License: archai is MIT, hub is Apache-2.0; Tags unique to archai: model-compression, automl, deep-learning, nas; More recently updated (last pushed Nov 24, 2025). ### When should I choose hub over archai? Choose hub over archai when License: hub is Apache-2.0, archai is MIT; Tags unique to hub: ml, embeddings, python, image-classification; Also covers Vector Databases, Computer Vision. ### When should I avoid archai? Last GitHub push was 230 days ago (slowing maintenance, Nov 24, 2025). Validate activity before betting a new project on archai. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### When should I avoid hub? Last GitHub push was 540 days ago (dormant maintenance, Jan 17, 2025). Validate activity before betting a new project on hub. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### Is archai or hub more popular on GitHub? hub has more GitHub stars (3,521 vs 485). Stars measure visibility, not whether either tool fits your constraints. ### Are archai and hub open source? Yes - both are open-source projects on GitHub (archai: MIT, hub: Apache-2.0). ### Where can I find alternatives to archai or hub? GraphCanon lists graph-backed alternatives at [archai alternatives](/tools/microsoft-archai/alternatives) and [hub alternatives](/tools/tensorflow-hub/alternatives) ([archai markdown twin](/tools/microsoft-archai/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/microsoft-archai-vs-tensorflow-hub.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, archai or hub? archai: 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 archai and hub? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [archai trust report](/tools/microsoft-archai/trust); [hub trust report](/tools/tensorflow-hub/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=microsoft-archai`](/api/graphcanon/graph?tool=microsoft-archai) - 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/_