Comparison
hopsworks vs awesome-AutoML
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.
Markdown twin · hopsworks alternatives · awesome-AutoML alternatives
GraphCanon updated Oct 4, 2026
vs
Trust & integrity
| Signal | hopsworks | awesome-AutoML |
|---|---|---|
| Maintenance | Dormant (595d since push) As of Sep 28, 2026 · github_public_v1 | Slowing (194d since push) As of Oct 4, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 28, 2026 · github_public_v1 | Not a fork · Personal account As of Oct 4, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) 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
- hopsworks
- Data-Intensive AI platform with Feature Store
- awesome-AutoML
- Curating AutoML research and resources
Stars
- hopsworks
- 1.3k
- awesome-AutoML
- 944
Forks
- hopsworks
- 159
- awesome-AutoML
- 157
Open issues
- hopsworks
- 16
- awesome-AutoML
- 1
Language
- hopsworks
- Java
- awesome-AutoML
- -
Adopt for
- hopsworks
- Hopsworks, an ML platform with robust data management and model serving capabilities, supports multiple cloud environments like AWS, Azure, and GCP.
- awesome-AutoML
- Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.
Persona
- hopsworks
- -
- awesome-AutoML
- -
Runtime
- hopsworks
- -
- awesome-AutoML
- -
License
- hopsworks
- AGPL-3.0
- awesome-AutoML
- GPL-3.0
Last pushed
- hopsworks
- Feb 10, 2025
- awesome-AutoML
- Mar 24, 2026
Categories
- hopsworks
- Evaluation & Observability, Inference & Serving, Model Training
- awesome-AutoML
- Model Training
Trust and health
Maintenance
- hopsworks
- Dormant (18%)
- awesome-AutoML
- Slowing (36%)
Days since push
- hopsworks
- 595d
- awesome-AutoML
- 194d
Open issues (now)
- hopsworks
- 16
- awesome-AutoML
- 1
Stars delta
- hopsworks
- +5 (30d)
- awesome-AutoML
- +3 (30d)
Owner type
- hopsworks
- Organization
- awesome-AutoML
- User
Full report
- hopsworks
- Trust report
- awesome-AutoML
- Trust report
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
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
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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (logicalclocks/hopsworks) · observed Sep 28, 2026
- GitHub forks (logicalclocks/hopsworks) · observed Sep 28, 2026
- Last push (logicalclocks/hopsworks) · observed Feb 10, 2025
- License file (AGPL-3.0) · observed Sep 28, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (windmaple/awesome-AutoML) · observed Oct 4, 2026
- GitHub forks (windmaple/awesome-AutoML) · observed Oct 4, 2026
- Last push (windmaple/awesome-AutoML) · observed Mar 24, 2026
- License file (GPL-3.0) · observed Oct 4, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: hopsworks 1.3k · awesome-AutoML 944 (synced Sep 28, 2026).
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 and awesome-AutoML alternatives (hopsworks markdown twin, awesome-AutoML 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, 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; awesome-AutoML trust report.