Comparison
seldon-core vs Awesome-LLMOps
Verdict
Pick seldon-core if seldon-core is an MLOps framework designed for managing machine learning models in Kubernetes environments; pick Awesome-LLMOps if awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more.
Markdown twin · seldon-core alternatives · Awesome-LLMOps alternatives
GraphCanon updated 4d
vs
Trust & integrity
| Signal | seldon-core | Awesome-LLMOps |
|---|---|---|
| Maintenance | Slowing (133d since push) As of 3w · github_public_v1 | Slowing (91d since push) As of 4d · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3w · github_public_v1 | Not a fork · Organization account As of 4d · 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-LLMOps
- An awesome & curated list of best LLMOps tools for developers
Stars
- seldon-core
- 4.8k
- Awesome-LLMOps
- 5.9k
Forks
- seldon-core
- 867
- Awesome-LLMOps
- 993
Open issues
- seldon-core
- 396
- Awesome-LLMOps
- 247
Language
- seldon-core
- Go
- Awesome-LLMOps
- Shell
Adopt for
- seldon-core
- seldon-core is an MLOps framework designed for managing machine learning models in Kubernetes environments.
- Awesome-LLMOps
- Awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more.
Persona
- seldon-core
- -
- Awesome-LLMOps
- -
Runtime
- seldon-core
- -
- Awesome-LLMOps
- -
License
- seldon-core
- SeldonIO/seldon-core uses The Business Source License for distribution
- Awesome-LLMOps
- CC0-1.0
Last pushed
- seldon-core
- Mar 23, 2026
- Awesome-LLMOps
- May 21, 2026
Categories
- seldon-core
- Inference & Serving
- Awesome-LLMOps
- Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio
Trust and health
Days since push
- seldon-core
- 133d
- Awesome-LLMOps
- 91d
Open issues (now)
- seldon-core
- 396
- Awesome-LLMOps
- 247
Stars delta
- seldon-core
- Unknown
- Awesome-LLMOps
- +28 (30d)
Open issues delta
- seldon-core
- Unknown
- Awesome-LLMOps
- +66 (30d)
Full report
- seldon-core
- Trust report
- Awesome-LLMOps
- Trust report
Choose seldon-core if…
- seldon-core is primarily Go; Awesome-LLMOps is Shell.
- License: seldon-core is Other, Awesome-LLMOps is CC0-1.0.
- 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.
Choose Awesome-LLMOps if…
- Awesome-LLMOps is primarily Shell; seldon-core is Go.
- License: Awesome-LLMOps is CC0-1.0, seldon-core is Other.
- Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops.
- Also covers Computer Vision, Data & Retrieval, Evaluation & Observability, LLM Frameworks, Model Training, Speech & Audio.
- - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management.
When NOT to use Awesome-LLMOps
- - When you are looking for a hands-on platform or framework for developing and deploying models rather than just a resource list.
- - If your focus is on general artificial intelligence development that includes areas beyond LLMOps like image processing, robotics, or federated learning without the need for LLM-specific resources.
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 (tensorchord/Awesome-LLMOps) · observed Aug 20, 2026
- GitHub forks (tensorchord/Awesome-LLMOps) · observed Aug 20, 2026
- Last push (tensorchord/Awesome-LLMOps) · observed May 21, 2026
- License file (CC0-1.0) · observed Aug 20, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: seldon-core 4.8k · Awesome-LLMOps 5.9k (synced Aug 3, 2026).
Common questions
- What is the difference between seldon-core and Awesome-LLMOps?
- seldon-core: An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models. Awesome-LLMOps: An awesome & curated list of best LLMOps tools for developers. See the comparison table for live GitHub stats and shared categories.
- When should I choose seldon-core over Awesome-LLMOps?
- Choose seldon-core over Awesome-LLMOps when seldon-core is primarily Go; Awesome-LLMOps is Shell; License: seldon-core is Other, Awesome-LLMOps is CC0-1.0; 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 choose Awesome-LLMOps over seldon-core?
- Choose Awesome-LLMOps over seldon-core when Awesome-LLMOps is primarily Shell; seldon-core is Go; License: Awesome-LLMOps is CC0-1.0, seldon-core is Other; Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops; Also covers Computer Vision, Data & Retrieval, Evaluation & Observability, LLM Frameworks, Model Training, Speech & Audio; - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management.
- 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-LLMOps?
- - When you are looking for a hands-on platform or framework for developing and deploying models rather than just a resource list. - If your focus is on general artificial intelligence development that includes areas beyond LLMOps like image processing, robotics, or federated learning without the need for LLM-specific resources.
- Is seldon-core or Awesome-LLMOps more popular on GitHub?
- Awesome-LLMOps has more GitHub stars (5,915 vs 4,765). Stars measure visibility, not whether either tool fits your constraints.
- Are seldon-core and Awesome-LLMOps open source?
- Yes - both are open-source projects on GitHub (seldon-core: Other, Awesome-LLMOps: CC0-1.0).
- Where can I find alternatives to seldon-core or Awesome-LLMOps?
- GraphCanon lists graph-backed alternatives at seldon-core alternatives and Awesome-LLMOps alternatives (seldon-core markdown twin, Awesome-LLMOps 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-LLMOps?
- seldon-core: Slowing. Awesome-LLMOps: 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 seldon-core and Awesome-LLMOps?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: seldon-core trust report; Awesome-LLMOps trust report.