Comparison
awesome-mlops vs seldon-core
Verdict
Pick awesome-mlops if awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML; pick seldon-core if seldon-core is an MLOps framework designed for managing machine learning models in Kubernetes environments.
Markdown twin · awesome-mlops alternatives · seldon-core alternatives
GraphCanon updated 3w
vs
Trust & integrity
| Signal | awesome-mlops | seldon-core |
|---|---|---|
| Maintenance | Slowing (97d since push) As of 3w · github_public_v1 | Slowing (133d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 3w · github_public_v1 | Not a fork · Organization account As of 3w · 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
- awesome-mlops
- A curated list of awesome MLOps tools.
- seldon-core
- An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
Stars
- awesome-mlops
- 5.2k
- seldon-core
- 4.8k
Forks
- awesome-mlops
- 762
- seldon-core
- 867
Open issues
- awesome-mlops
- 71
- seldon-core
- 396
Language
- awesome-mlops
- Python
- seldon-core
- Go
Adopt for
- awesome-mlops
- Awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML.
- seldon-core
- seldon-core is an MLOps framework designed for managing machine learning models in Kubernetes environments.
Persona
- awesome-mlops
- -
- seldon-core
- -
Runtime
- awesome-mlops
- -
- seldon-core
- -
License
- awesome-mlops
- -
- seldon-core
- SeldonIO/seldon-core uses The Business Source License for distribution
Last pushed
- awesome-mlops
- Apr 29, 2026
- seldon-core
- Mar 23, 2026
Categories
- awesome-mlops
- Developer Tools, Evaluation & Observability, Inference & Serving, Model Training
- seldon-core
- Inference & Serving
Trust and health
Days since push
- awesome-mlops
- 97d
- seldon-core
- 133d
Open issues (now)
- awesome-mlops
- 71
- seldon-core
- 396
Owner type
- awesome-mlops
- User
- seldon-core
- Organization
Full report
- awesome-mlops
- Trust report
- seldon-core
- Trust report
Choose awesome-mlops if…
- awesome-mlops is primarily Python; seldon-core is Go.
- Tags unique to awesome-mlops: ai, awesome, data-science, machine-learning.
- Also covers Developer Tools, Evaluation & Observability, Model Training.
- You need resources across multiple facets of the machine-learning pipeline, from data validation to model serving.
When NOT to use awesome-mlops
- In search of a single comprehensive tool for end-to-end ML project management; Awesome MLOps is a repository of links rather than a standalone platform.
- Looking for proprietary solutions or detailed vendor-specific documentation as it focuses on broad, open-source offerings.
Choose seldon-core if…
- seldon-core is primarily Go; awesome-mlops is Python.
- 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (kelvins/awesome-mlops) · observed Aug 4, 2026
- GitHub forks (kelvins/awesome-mlops) · observed Aug 4, 2026
- Last push (kelvins/awesome-mlops) · observed Apr 29, 2026
- License file (unknown) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- 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 on cards: awesome-mlops 5.2k · seldon-core 4.8k (synced Aug 4, 2026).
Common questions
- What is the difference between awesome-mlops and seldon-core?
- awesome-mlops: A curated list of awesome MLOps tools.. seldon-core: An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models. See the comparison table for live GitHub stats and shared categories.
- When should I choose awesome-mlops over seldon-core?
- Choose awesome-mlops over seldon-core when awesome-mlops is primarily Python; seldon-core is Go; Tags unique to awesome-mlops: ai, awesome, data-science, machine-learning; Also covers Developer Tools, Evaluation & Observability, Model Training; You need resources across multiple facets of the machine-learning pipeline, from data validation to model serving.
- When should I choose seldon-core over awesome-mlops?
- Choose seldon-core over awesome-mlops when seldon-core is primarily Go; awesome-mlops is Python; 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 avoid awesome-mlops?
- In search of a single comprehensive tool for end-to-end ML project management; Awesome MLOps is a repository of links rather than a standalone platform. Looking for proprietary solutions or detailed vendor-specific documentation as it focuses on broad, open-source offerings.
- 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.
- Is awesome-mlops or seldon-core more popular on GitHub?
- awesome-mlops has more GitHub stars (5,229 vs 4,765). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-mlops and seldon-core open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to awesome-mlops or seldon-core?
- GraphCanon lists graph-backed alternatives at awesome-mlops alternatives and seldon-core alternatives (awesome-mlops markdown twin, seldon-core 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, awesome-mlops or seldon-core?
- awesome-mlops: Slowing. seldon-core: 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 awesome-mlops and seldon-core?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-mlops trust report; seldon-core trust report.