--- title: "awesome-mlops vs skypilot" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/kelvins-awesome-mlops-vs-skypilot-org-skypilot" tools: ["kelvins-awesome-mlops", "skypilot-org-skypilot"] --- # awesome-mlops vs skypilot *GraphCanon updated Aug 7, 2026* ## Verdict Pick awesome-mlops if awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML; pick skypilot if skyPilot is a Python-based platform for managing AI workloads across diverse cloud and on-premises environments. It supports deep learning tasks such as distributed training, hyperparameter tuning, and model serving. [awesome-mlops](https://github.com/kelvins/awesome-mlops) reports 5.2k GitHub stars, 762 forks, and 71 open issues, last pushed Apr 29, 2026. [skypilot](https://skypilot.ai/) has 10k stars, 1.2k forks, and 344 open issues, last pushed Aug 7, 2026. Figures are from public GitHub metadata via [awesome-mlops's repository](https://github.com/kelvins/awesome-mlops) and [skypilot's repository](https://github.com/skypilot-org/skypilot). | | [awesome-mlops](/tools/kelvins-awesome-mlops.md) | [skypilot](/tools/skypilot-org-skypilot.md) | | --- | --- | --- | | Tagline | A curated list of awesome MLOps tools. | Run, manage, and scale AI workloads on any AI infrastructure. | | Stars | 5,229 | 10,456 | | Forks | 762 | 1,175 | | Open issues | 71 | 344 | | Language | Python | Python | | Adopt for | Awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML. | SkyPilot is a Python-based platform for managing AI workloads across diverse cloud and on-premises environments. It supports deep learning tasks such as distributed training, hyperparameter tuning, and model serving. | | Persona | - | - | | Runtime | - | - | | License | - | Apache-2.0 | | Categories | Developer Tools, Evaluation & Observability, Inference & Serving, Model Training | Developer Tools, Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-mlops](/tools/kelvins-awesome-mlops.md) | [skypilot](/tools/skypilot-org-skypilot.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Very active (96%) | | Days since push | 97d | 0d | | Open issues (now) | 71 | 344 | | Owner type | User | Organization | | Full report | [trust report](/tools/kelvins-awesome-mlops/trust.md) | [trust report](/tools/skypilot-org-skypilot/trust.md) | ## Shared compatibility - **Python**: [awesome-mlops](/tools/kelvins-awesome-mlops.md) - Python runtime; [skypilot](/tools/skypilot-org-skypilot.md) - Python runtime ## Decision facts: awesome-mlops - **Adopt for:** Awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML. ## Decision facts: skypilot - **Pricing:** freemium - SkyPilot operates under an open-source license (Apache-2.0) with core features available freely, while advanced optimizations and integrations may drive usage towards higher costs based on underlying云 - **Adopt for:** SkyPilot is a Python-based platform for managing AI workloads across diverse cloud and on-premises environments. It supports deep learning tasks such as distributed training, hyperparameter tuning, and model serving. ## Choose when ### Choose awesome-mlops if… - Tags unique to awesome-mlops: ai, awesome, data-science, machine-learning. - Also covers Evaluation & Observability. - You need resources across multiple facets of the machine-learning pipeline, from data validation to model serving. ### Choose skypilot if… - Pricing: SkyPilot operates under an open-source license (Apache-2.0) with core features available freely, while advanced optimizations and integrations may drive usage towards higher costs based on underlying云. - Tags unique to skypilot: cloud-computing, cloud-management, cost-optimization, deep-learning. - skypilot ships Docker support for self-hosted deployment. - When you need to manage multiple cloud resources including Kubernetes clusters, Slurm, and over 20 different clouds along with on-premise servers. ## 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. ## When NOT to use skypilot - Avoid SkyPilot if you are working exclusively on a single cloud platform without a need for multi-cloud resource management or optimization. - Not recommended if your primary requirement is a specialized training algorithm that lacks support within the Python environment or the limitations of existing SkyPilot capabilities. ## Common questions ### What is the difference between awesome-mlops and skypilot? awesome-mlops: A curated list of awesome MLOps tools.. skypilot: Run, manage, and scale AI workloads on any AI infrastructure.. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome-mlops over skypilot? Choose awesome-mlops over skypilot when Tags unique to awesome-mlops: ai, awesome, data-science, machine-learning; Also covers Evaluation & Observability; You need resources across multiple facets of the machine-learning pipeline, from data validation to model serving. ### When should I choose skypilot over awesome-mlops? Choose skypilot over awesome-mlops when Pricing: SkyPilot operates under an open-source license (Apache-2.0) with core features available freely, while advanced optimizations and integrations may drive usage towards higher costs based on underlying云; Tags unique to skypilot: cloud-computing, cloud-management, cost-optimization, deep-learning; skypilot ships Docker support for self-hosted deployment; When you need to manage multiple cloud resources including Kubernetes clusters, Slurm, and over 20 different clouds along with on-premise servers. ### 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 skypilot? Avoid SkyPilot if you are working exclusively on a single cloud platform without a need for multi-cloud resource management or optimization. Not recommended if your primary requirement is a specialized training algorithm that lacks support within the Python environment or the limitations of existing SkyPilot capabilities. ### Is awesome-mlops or skypilot more popular on GitHub? skypilot has more GitHub stars (10,456 vs 5,229). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-mlops and skypilot open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to awesome-mlops or skypilot? GraphCanon lists graph-backed alternatives at [awesome-mlops alternatives](/tools/kelvins-awesome-mlops/alternatives) and [skypilot alternatives](/tools/skypilot-org-skypilot/alternatives) ([awesome-mlops markdown twin](/tools/kelvins-awesome-mlops/alternatives.md), [skypilot markdown twin](/tools/skypilot-org-skypilot/alternatives.md)), 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](/compare/kelvins-awesome-mlops-vs-skypilot-org-skypilot.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, awesome-mlops or skypilot? awesome-mlops: Slowing. skypilot: 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 awesome-mlops and skypilot? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-mlops trust report](/tools/kelvins-awesome-mlops/trust); [skypilot trust report](/tools/skypilot-org-skypilot/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=kelvins-awesome-mlops`](/api/graphcanon/graph?tool=kelvins-awesome-mlops) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_