Comparison
feast vs datasets
Verdict
Pick feast if feast, an open-source feature store for AI/ML, facilitates efficient management and retrieval of features used in machine learning models; pick datasets if datasets is the largest hub of ready-to-use datasets for AI models, offering extensive collection and fast, easy-to-use data manipulation tools.
Markdown twin · feast alternatives · datasets alternatives
GraphCanon updated 2w
Trust & integrity
| Signal | feast | datasets |
|---|---|---|
| Maintenance | Very active (2d since push) As of 2w · github_public_v1 | Very active (0d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Organization account As of 3w · github_public_v1 |
| OSV dependency advisories | Published findings As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · 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
- feast
- The Open Source Feature Store for AI/ML
- datasets
- Largest hub of ready-to-use datasets for AI models
Stars
- feast
- 7.2k
- datasets
- 22k
Forks
- feast
- 1.4k
- datasets
- 3.3k
Open issues
- feast
- 390
- datasets
- 1.2k
Language
- feast
- Python
- datasets
- Python
Adopt for
- feast
- Feast, an open-source feature store for AI/ML, facilitates efficient management and retrieval of features used in machine learning models.
- datasets
- datasets is the largest hub of ready-to-use datasets for AI models, offering extensive collection and fast, easy-to-use data manipulation tools.
Persona
- feast
- -
- datasets
- -
Runtime
- feast
- -
- datasets
- -
License
- feast
- Apache-2.0
- datasets
- Apache-2.0
Last pushed
- feast
- Jul 31, 2026
- datasets
- Jul 30, 2026
Categories
- feast
- Data & Retrieval
- datasets
- Data & Retrieval
Trust and health
Days since push
- feast
- 2d
- datasets
- 0d
Open issues (now)
- feast
- 390
- datasets
- 1.2k
OSV dependency advisories
- feast
- Published findings
- datasets
- No lockfile (source not queried)
Full report
- feast
- Trust report
- datasets
- Trust report
Shared compatibility
- Python · feast: Python runtime · datasets: Python runtime
Choose feast if…
- Tags unique to feast: big-data, data-engineering, data-quality, data-science.
- Use Feast when your project requires versioning of features to support experimentation and model evolution over time, as it allows you to seamlessly retrieve historical feature data.
- More recently updated (last pushed Jul 31, 2026).
When NOT to use feast
- Avoid Feast in scenarios where your project needs are minimal, such as smaller datasets or simpler projects that do not require the overhead of feature versioning or management.
- Do not use Feast if you prefer a more generalized data storage solution without specific features geared towards ML feature management. Competitors might be better for broader data manipulation tasks.
Choose datasets if…
- Tags unique to datasets: ai, artificial-intelligence, dataset-hub, datasets.
- Use datasets if you need access to a large number of ready-to-use datasets specifically suited for training AI models.
- More GitHub stars (22k vs 7.2k) - visibility, not fit.
When NOT to use datasets
- Avoid datasets if the specific type of dataset required for your project is not included in their extensive collection.
- Do not use datasets if you prefer less integration with popular machine learning frameworks like PyTorch or TensorFlow, as this tool heavily integrates with these platforms.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (feast-dev/feast) · observed Aug 3, 2026
- GitHub forks (feast-dev/feast) · observed Aug 3, 2026
- Last push (feast-dev/feast) · observed Jul 31, 2026
- License file (Apache-2.0) · observed Aug 3, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (huggingface/datasets) · observed Jul 31, 2026
- GitHub forks (huggingface/datasets) · observed Jul 31, 2026
- Last push (huggingface/datasets) · observed Jul 30, 2026
- License file (Apache-2.0) · observed Jul 31, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: feast 7.2k · datasets 22k (synced Aug 3, 2026).
Common questions
- What is the difference between feast and datasets?
- feast: The Open Source Feature Store for AI/ML. datasets: Largest hub of ready-to-use datasets for AI models. See the comparison table for live GitHub stats and shared categories.
- When should I choose feast over datasets?
- Choose feast over datasets when Tags unique to feast: big-data, data-engineering, data-quality, data-science; Use Feast when your project requires versioning of features to support experimentation and model evolution over time, as it allows you to seamlessly retrieve historical feature data; More recently updated (last pushed Jul 31, 2026).
- When should I choose datasets over feast?
- Choose datasets over feast when Tags unique to datasets: ai, artificial-intelligence, dataset-hub, datasets; Use datasets if you need access to a large number of ready-to-use datasets specifically suited for training AI models; More GitHub stars (22k vs 7.2k) - visibility, not fit.
- When should I avoid feast?
- Avoid Feast in scenarios where your project needs are minimal, such as smaller datasets or simpler projects that do not require the overhead of feature versioning or management. Do not use Feast if you prefer a more generalized data storage solution without specific features geared towards ML feature management. Competitors might be better for broader data manipulation tasks.
- When should I avoid datasets?
- Avoid datasets if the specific type of dataset required for your project is not included in their extensive collection. Do not use datasets if you prefer less integration with popular machine learning frameworks like PyTorch or TensorFlow, as this tool heavily integrates with these platforms.
- Is feast or datasets more popular on GitHub?
- datasets has more GitHub stars (21,791 vs 7,188). Stars measure visibility, not whether either tool fits your constraints.
- Are feast and datasets open source?
- Yes - both are open-source projects on GitHub (feast: Apache-2.0, datasets: Apache-2.0).
- Where can I find alternatives to feast or datasets?
- GraphCanon lists graph-backed alternatives at feast alternatives and datasets alternatives (feast markdown twin, datasets 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, feast or datasets?
- feast: Very active. datasets: 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 feast and datasets?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: feast trust report; datasets trust report.