Comparison
autokeras vs model-optimization
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 model-optimization if toolkit for optimizing ML models in Keras and TensorFlow, focusing on quantization and pruning.
Markdown twin · autokeras alternatives · model-optimization alternatives
GraphCanon updated Oct 3, 2026
15views this month
vs
Trust & integrity
| Signal | autokeras | model-optimization |
|---|---|---|
| Maintenance | Slowing (309d since push) As of Sep 30, 2026 · github_public_v1 | Very active (1d since push) As of Oct 3, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 30, 2026 · github_public_v1 | Not a fork · Organization account As of Oct 3, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | No published findings from this source as of 2026-07-11 As of Jul 11, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- autokeras
- AutoML library for deep learning
- model-optimization
- Toolkit for optimizing ML models in Keras and TensorFlow
Stars
- autokeras
- 9.3k
- model-optimization
- 1.6k
Forks
- autokeras
- 1.4k
- model-optimization
- 348
Open issues
- autokeras
- 161
- model-optimization
- 252
Language
- autokeras
- Python
- model-optimization
- Python
Adopt for
- autokeras
- AutoKeras simplifies deep learning model design through automated neural architecture search and is compatible with Python 3.7+ and TensorFlow 2.8.0+.
- model-optimization
- Toolkit for optimizing ML models in Keras and TensorFlow, focusing on quantization and pruning.
Persona
- autokeras
- -
- model-optimization
- -
Runtime
- autokeras
- -
- model-optimization
- -
License
- autokeras
- Apache-2.0
- model-optimization
- Apache-2.0
Last pushed
- autokeras
- Nov 25, 2025
- model-optimization
- Oct 2, 2026
Categories
- autokeras
- Developer Tools, Model Training
- model-optimization
- Model Training
Trust and health
Maintenance
- autokeras
- Slowing (36%)
- model-optimization
- Very active (96%)
Days since push
- autokeras
- 309d
- model-optimization
- 1d
Open issues (now)
- autokeras
- 161
- model-optimization
- 252
Stars delta
- autokeras
- +7 (30d)
- model-optimization
- +3 (30d)
Open issues delta
- autokeras
- 0 (30d)
- model-optimization
- +6 (30d)
OSV dependency advisories
- autokeras
- No lockfile (source not queried)
- model-optimization
- No published findings from this source as of 2026-07-11
Full report
- autokeras
- Trust report
- model-optimization
- Trust report
Choose autokeras if…
- Tags unique to autokeras: autodl, automl, neural-architecture-search, tensorflow.
- Also covers Developer Tools.
- When your project involves deep learning tasks requiring minimal manual intervention in designing models.
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.
Choose model-optimization if…
- Tags unique to model-optimization: compression, ml, model-compression, optimization.
- When you are working with Keras or TensorFlow models and need to apply post-training quantization or pruning techniques to minimize model size and enhance inference speed.
- More recently updated (last pushed Oct 2, 2026).
When NOT to use model-optimization
- Do not use this toolkit if you are working with ML models outside of Keras and TensorFlow frameworks, as it does not support other popular frameworks like PyTorch.
- Avoid using this toolkit when detailed customization is needed beyond its quantization and pruning options, since the available methods might be too limited for complex optimization tasks.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (keras-team/autokeras) · observed Sep 30, 2026
- GitHub forks (keras-team/autokeras) · observed Sep 30, 2026
- Last push (keras-team/autokeras) · observed Nov 25, 2025
- License file (Apache-2.0) · observed Sep 30, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (tensorflow/model-optimization) · observed Oct 3, 2026
- GitHub forks (tensorflow/model-optimization) · observed Oct 3, 2026
- Last push (tensorflow/model-optimization) · observed Oct 2, 2026
- License file (Apache-2.0) · observed Oct 3, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: autokeras 9.3k · model-optimization 1.6k (synced Sep 30, 2026).
Common questions
- What is the difference between autokeras and model-optimization?
- autokeras: AutoML library for deep learning. model-optimization: Toolkit for optimizing ML models in Keras and TensorFlow. See the comparison table for live GitHub stats and shared categories.
- When should I choose autokeras over model-optimization?
- Choose autokeras over model-optimization when Tags unique to autokeras: autodl, automl, neural-architecture-search, tensorflow; Also covers Developer Tools; When your project involves deep learning tasks requiring minimal manual intervention in designing models.
- When should I choose model-optimization over autokeras?
- Choose model-optimization over autokeras when Tags unique to model-optimization: compression, ml, model-compression, optimization; When you are working with Keras or TensorFlow models and need to apply post-training quantization or pruning techniques to minimize model size and enhance inference speed; More recently updated (last pushed Oct 2, 2026).
- 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 model-optimization?
- Do not use this toolkit if you are working with ML models outside of Keras and TensorFlow frameworks, as it does not support other popular frameworks like PyTorch. Avoid using this toolkit when detailed customization is needed beyond its quantization and pruning options, since the available methods might be too limited for complex optimization tasks.
- Is autokeras or model-optimization more popular on GitHub?
- autokeras has more GitHub stars (9,335 vs 1,579). Stars measure visibility, not whether either tool fits your constraints.
- Are autokeras and model-optimization open source?
- Yes - both are open-source projects on GitHub (autokeras: Apache-2.0, model-optimization: Apache-2.0).
- Where can I find alternatives to autokeras or model-optimization?
- GraphCanon lists graph-backed alternatives at autokeras alternatives and model-optimization alternatives (autokeras markdown twin, model-optimization markdown twin), 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 mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, autokeras or model-optimization?
- autokeras: Slowing. model-optimization: Very active. 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 model-optimization?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: autokeras trust report; model-optimization trust report.