Comparison
awesome-open-mlops vs seldon-core
Verdict
Pick awesome-open-mlops if awesome-open-mlops highlights open-source MLOps tools specifically for model deployment and serving, offering a guide curated by Fuzzy Labs; pick seldon-core if seldon-core is an MLOps framework designed for managing machine learning models in Kubernetes environments.
Markdown twin · awesome-open-mlops alternatives · seldon-core alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | awesome-open-mlops | seldon-core |
|---|---|---|
| Maintenance | Dormant (442d since push) As of 2w · github_public_v1 | Slowing (133d 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 | 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-open-mlops
- Model deployment and serving guide with open-source MLOps tools
- seldon-core
- An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
Stars
- awesome-open-mlops
- 482
- seldon-core
- 4.8k
Forks
- awesome-open-mlops
- 54
- seldon-core
- 867
Open issues
- awesome-open-mlops
- 6
- seldon-core
- 396
Language
- awesome-open-mlops
- -
- seldon-core
- Go
Adopt for
- awesome-open-mlops
- awesome-open-mlops highlights open-source MLOps tools specifically for model deployment and serving, offering a guide curated by Fuzzy Labs.
- seldon-core
- seldon-core is an MLOps framework designed for managing machine learning models in Kubernetes environments.
Persona
- awesome-open-mlops
- -
- seldon-core
- -
Runtime
- awesome-open-mlops
- -
- seldon-core
- -
License
- awesome-open-mlops
- Apache 2.0 licensed, compatible with other Apache software, promoting free use in both commercial and non-commercial contexts.
- seldon-core
- SeldonIO/seldon-core uses The Business Source License for distribution
Last pushed
- awesome-open-mlops
- May 19, 2025
- seldon-core
- Mar 23, 2026
Categories
- awesome-open-mlops
- Inference & Serving
- seldon-core
- Inference & Serving
Trust and health
Maintenance
- awesome-open-mlops
- Dormant (18%)
- seldon-core
- Slowing (36%)
Days since push
- awesome-open-mlops
- 442d
- seldon-core
- 133d
Open issues (now)
- awesome-open-mlops
- 6
- seldon-core
- 396
Full report
- awesome-open-mlops
- Trust report
- seldon-core
- Trust report
Choose awesome-open-mlops if…
- License: awesome-open-mlops is Apache-2.0, seldon-core is Other.
- No specific details available.
- Pricing: `awesome-open-mlops` is freely accessible but depends on the community for updates and content contributions. No paid services are associated with this repository, making it purely a curated resource..
- Tags unique to awesome-open-mlops: datascience, devops, infrastructure, machine-learning.
- When seeking a comprehensive list of open-source models focused on deploying and serving ML models for REST API use cases
When NOT to use awesome-open-mlops
- Avoid if you need proprietary or commercial MLOps solutions that offer enterprise support or features not covered by open-source projects
- Not suitable for scenarios where model serving frameworks outside of the curated list, such as those under different licenses like AGPL-3.0 used by BodyworkML, are required
Choose seldon-core if…
- License: seldon-core is Other, awesome-open-mlops is Apache-2.0.
- 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 (fuzzylabs/awesome-open-mlops) · observed Aug 4, 2026
- GitHub forks (fuzzylabs/awesome-open-mlops) · observed Aug 4, 2026
- Last push (fuzzylabs/awesome-open-mlops) · observed May 19, 2025
- License file (Apache-2.0) · 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-open-mlops 482 · seldon-core 4.8k (synced Aug 4, 2026).
Common questions
- What is the difference between awesome-open-mlops and seldon-core?
- awesome-open-mlops: Model deployment and serving guide with open-source 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-open-mlops over seldon-core?
- Choose awesome-open-mlops over seldon-core when License: awesome-open-mlops is Apache-2.0, seldon-core is Other; No specific details available; Pricing:
awesome-open-mlopsis freely accessible but depends on the community for updates and content contributions. No paid services are associated with this repository, making it purely a curated resource.; Tags unique to awesome-open-mlops: datascience, devops, infrastructure, machine-learning; When seeking a comprehensive list of open-source models focused on deploying and serving ML models for REST API use cases. - When should I choose seldon-core over awesome-open-mlops?
- Choose seldon-core over awesome-open-mlops when License: seldon-core is Other, awesome-open-mlops is Apache-2.0; 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-open-mlops?
- Avoid if you need proprietary or commercial MLOps solutions that offer enterprise support or features not covered by open-source projects Not suitable for scenarios where model serving frameworks outside of the curated list, such as those under different licenses like AGPL-3.0 used by BodyworkML, are required
- 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-open-mlops or seldon-core more popular on GitHub?
- seldon-core has more GitHub stars (4,765 vs 482). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-open-mlops and seldon-core open source?
- Yes - both are open-source projects on GitHub (awesome-open-mlops: Apache-2.0, seldon-core: Other).
- Where can I find alternatives to awesome-open-mlops or seldon-core?
- GraphCanon lists graph-backed alternatives at awesome-open-mlops alternatives and seldon-core alternatives (awesome-open-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-open-mlops or seldon-core?
- awesome-open-mlops: Dormant. 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-open-mlops and seldon-core?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-open-mlops trust report; seldon-core trust report.