--- title: "awesome-mlops vs primehub" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/kelvins-awesome-mlops-vs-myelintek-primehub" tools: ["kelvins-awesome-mlops", "myelintek-primehub"] --- # awesome-mlops vs primehub *GraphCanon updated Aug 4, 2026* ## Verdict Pick awesome-mlops if awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML; pick primehub if suitable for teams needing an open-source MLOps platform with robust support for distributed systems and Docker environments. [awesome-mlops](https://github.com/kelvins/awesome-mlops) reports 5.2k GitHub stars, 762 forks, and 71 open issues, last pushed Apr 29, 2026. [primehub](https://docs.primehub.io) has 410 stars, 40 forks, and 28 open issues, last pushed Jan 13, 2026. Figures are from public GitHub metadata via [awesome-mlops's repository](https://github.com/kelvins/awesome-mlops) and [primehub's repository](https://github.com/myelintek/primehub). | | [awesome-mlops](/tools/kelvins-awesome-mlops.md) | [primehub](/tools/myelintek-primehub.md) | | --- | --- | --- | | Tagline | A curated list of awesome MLOps tools. | open-source MLOps platform | | Stars | 5,229 | 410 | | Forks | 762 | 40 | | Open issues | 71 | 28 | | Language | Python | Shell | | Adopt for | Awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML. | Suitable for teams needing an open-source MLOps platform with robust support for distributed systems and Docker environments. | | Persona | - | - | | Runtime | - | - | | License | - | Apache-2.0 | | Categories | Developer Tools, Evaluation & Observability, Inference & Serving, Model Training | Developer Tools, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-mlops](/tools/kelvins-awesome-mlops.md) | [primehub](/tools/myelintek-primehub.md) | | --- | --- | --- | | Days since push | 97d | 201d | | Open issues (now) | 71 | 28 | | Owner type | User | Organization | | Full report | [trust report](/tools/kelvins-awesome-mlops/trust.md) | [trust report](/tools/myelintek-primehub/trust.md) | ## Decision facts: awesome-mlops - **Adopt for:** Awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML. ## Decision facts: primehub - **Adopt for:** Suitable for teams needing an open-source MLOps platform with robust support for distributed systems and Docker environments. ## Choose when ### Choose awesome-mlops if… - awesome-mlops is primarily Python; primehub is Shell. - Tags unique to awesome-mlops: ai, awesome, machine-learning-engineering, ml. - Also covers Evaluation & Observability, Inference & Serving. - You need resources across multiple facets of the machine-learning pipeline, from data validation to model serving. ### Choose primehub if… - primehub is primarily Shell; awesome-mlops is Python. - Tags unique to primehub: distributed-systems, docker, jupyter, jupyterhub. - Utilize PrimeHub if your project requires integration of Kubernetes, as it supports orchestration within this framework. ## 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 primehub - Avoid if your project strictly mandates proprietary MLOps solutions over open-source alternatives. - Not appropriate for teams that do not operate within Docker environments, as significant customization might be required. ## Common questions ### What is the difference between awesome-mlops and primehub? awesome-mlops: A curated list of awesome MLOps tools.. primehub: open-source MLOps platform. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome-mlops over primehub? Choose awesome-mlops over primehub when awesome-mlops is primarily Python; primehub is Shell; Tags unique to awesome-mlops: ai, awesome, machine-learning-engineering, ml; Also covers Evaluation & Observability, Inference & Serving; You need resources across multiple facets of the machine-learning pipeline, from data validation to model serving. ### When should I choose primehub over awesome-mlops? Choose primehub over awesome-mlops when primehub is primarily Shell; awesome-mlops is Python; Tags unique to primehub: distributed-systems, docker, jupyter, jupyterhub; Utilize PrimeHub if your project requires integration of Kubernetes, as it supports orchestration within this framework. ### 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 primehub? Avoid if your project strictly mandates proprietary MLOps solutions over open-source alternatives. Not appropriate for teams that do not operate within Docker environments, as significant customization might be required. ### Is awesome-mlops or primehub more popular on GitHub? awesome-mlops has more GitHub stars (5,229 vs 410). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-mlops and primehub open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to awesome-mlops or primehub? GraphCanon lists graph-backed alternatives at [awesome-mlops alternatives](/tools/kelvins-awesome-mlops/alternatives) and [primehub alternatives](/tools/myelintek-primehub/alternatives) ([awesome-mlops markdown twin](/tools/kelvins-awesome-mlops/alternatives.md), [primehub markdown twin](/tools/myelintek-primehub/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-myelintek-primehub.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, awesome-mlops or primehub? awesome-mlops: Slowing. primehub: 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 primehub? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-mlops trust report](/tools/kelvins-awesome-mlops/trust); [primehub trust report](/tools/myelintek-primehub/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/_