Comparison
seldon-core vs awesome-mlops
Verdict
Pick seldon-core if seldon-core is an MLOps framework designed for managing machine learning models in Kubernetes environments; pick awesome-mlops if awesome-mlops curates MLOps resources focusing on diverse deployment strategies and tooling.
Markdown twin · seldon-core alternatives · awesome-mlops alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | seldon-core | awesome-mlops |
|---|---|---|
| Maintenance | Slowing (133d since push) As of 3w · github_public_v1 | Dormant (621d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3w · github_public_v1 | Not a fork · Personal account As of 2w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) 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
- seldon-core
- An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
- awesome-mlops
- A curated list of references for MLOps
Stars
- seldon-core
- 4.8k
- awesome-mlops
- 14k
Forks
- seldon-core
- 867
- awesome-mlops
- 2.1k
Open issues
- seldon-core
- 396
- awesome-mlops
- 44
Language
- seldon-core
- Go
- awesome-mlops
- -
Adopt for
- seldon-core
- seldon-core is an MLOps framework designed for managing machine learning models in Kubernetes environments.
- awesome-mlops
- awesome-mlops curates MLOps resources focusing on diverse deployment strategies and tooling.
Persona
- seldon-core
- -
- awesome-mlops
- -
Runtime
- seldon-core
- -
- awesome-mlops
- -
License
- seldon-core
- SeldonIO/seldon-core uses The Business Source License for distribution
- awesome-mlops
- -
Last pushed
- seldon-core
- Mar 23, 2026
- awesome-mlops
- Nov 21, 2024
Categories
- seldon-core
- Inference & Serving
- awesome-mlops
- Inference & Serving, Model Training
Trust and health
Maintenance
- seldon-core
- Slowing (36%)
- awesome-mlops
- Dormant (18%)
Days since push
- seldon-core
- 133d
- awesome-mlops
- 621d
Open issues (now)
- seldon-core
- 396
- awesome-mlops
- 44
Owner type
- seldon-core
- Organization
- awesome-mlops
- User
Full report
- seldon-core
- Trust report
- awesome-mlops
- Trust report
Choose seldon-core if…
- Requirements: Requires Docker; Requires Docker for deployment environments.
- Tags unique to seldon-core: aiops, deployment, kubernetes, machine-learning-operations.
- If you are deploying and serving ML models on Kubernetes clusters, seldon-core provides specialized capabilities within its MLOps framework to facilitate this.
When NOT to use seldon-core
- Consider alternatives if you are not running your infrastructure on Kubernetes, since seldon-core is optimized for Kubernetes environments.
- If compatibility or licensing concerns arise due to the Business Source License under which Seldon is distributed, explore other frameworks with more permissive licenses.
Choose awesome-mlops if…
- Tags unique to awesome-mlops: ai, data-science, devops, engineering.
- Also covers Model Training.
- If you need references covering online training and inference service architecture patterns, consider awesome-mlops.
When NOT to use awesome-mlops
- Avoid if focused solely on a single MLOps tool or framework as this is a broad resource list.
- Not suitable for those seeking end-to-end support beyond references, like hands-on deployment assistance.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (SeldonIO/seldon-core) · observed Aug 3, 2026
- GitHub forks (SeldonIO/seldon-core) · observed Aug 3, 2026
- Last push (SeldonIO/seldon-core) · observed Mar 23, 2026
- License file (Other) · observed Aug 3, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (visenger/awesome-mlops) · observed Aug 4, 2026
- GitHub forks (visenger/awesome-mlops) · observed Aug 4, 2026
- Last push (visenger/awesome-mlops) · observed Nov 21, 2024
- License file (unknown) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: seldon-core 4.8k · awesome-mlops 14k (synced Aug 3, 2026).
Common questions
- What is the difference between seldon-core and awesome-mlops?
- seldon-core: An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models. awesome-mlops: A curated list of references for MLOps. See the comparison table for live GitHub stats and shared categories.
- When should I choose seldon-core over awesome-mlops?
- Choose seldon-core over awesome-mlops when Requirements: Requires Docker; Requires Docker for deployment environments; Tags unique to seldon-core: aiops, deployment, kubernetes, machine-learning-operations; If you are deploying and serving ML models on Kubernetes clusters, seldon-core provides specialized capabilities within its MLOps framework to facilitate this.
- When should I choose awesome-mlops over seldon-core?
- Choose awesome-mlops over seldon-core when Tags unique to awesome-mlops: ai, data-science, devops, engineering; Also covers Model Training; If you need references covering online training and inference service architecture patterns, consider awesome-mlops.
- When should I avoid seldon-core?
- Consider alternatives if you are not running your infrastructure on Kubernetes, since seldon-core is optimized for Kubernetes environments. If compatibility or licensing concerns arise due to the Business Source License under which Seldon is distributed, explore other frameworks with more permissive licenses.
- When should I avoid awesome-mlops?
- Avoid if focused solely on a single MLOps tool or framework as this is a broad resource list. Not suitable for those seeking end-to-end support beyond references, like hands-on deployment assistance.
- Is seldon-core or awesome-mlops more popular on GitHub?
- awesome-mlops has more GitHub stars (14,127 vs 4,765). Stars measure visibility, not whether either tool fits your constraints.
- Are seldon-core and awesome-mlops open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to seldon-core or awesome-mlops?
- GraphCanon lists graph-backed alternatives at seldon-core alternatives and awesome-mlops alternatives (seldon-core markdown twin, awesome-mlops 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, seldon-core or awesome-mlops?
- seldon-core: Slowing. awesome-mlops: 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 seldon-core and awesome-mlops?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: seldon-core trust report; awesome-mlops trust report.