--- title: "awesome-mlops vs mlflow" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/kelvins-awesome-mlops-vs-mlflow-mlflow" tools: ["kelvins-awesome-mlops", "mlflow-mlflow"] --- # awesome-mlops vs mlflow *GraphCanon updated Aug 20, 2026* ## Verdict Pick awesome-mlops if awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML; pick mlflow if mLflow is an open-source platform that offers comprehensive capabilities for managing, deploying, and monitoring machine learning models as well as large language models (LLMs) and AI agents. MLflow supports various use,. [awesome-mlops](https://github.com/kelvins/awesome-mlops) reports 5.2k GitHub stars, 762 forks, and 71 open issues, last pushed Apr 29, 2026. [mlflow](https://mlflow.org) has 28k stars, 6.2k forks, and 2.1k open issues, last pushed Aug 20, 2026. Figures are from public GitHub metadata via [awesome-mlops's repository](https://github.com/kelvins/awesome-mlops) and [mlflow's repository](https://github.com/mlflow/mlflow). | | [awesome-mlops](/tools/kelvins-awesome-mlops.md) | [mlflow](/tools/mlflow-mlflow.md) | | --- | --- | --- | | Tagline | A curated list of awesome MLOps tools. | AI engineering platform for debugging, evaluating, monitoring, and optimizing AI applications | | Stars | 5,229 | 27,591 | | Forks | 762 | 6,189 | | Open issues | 71 | 2,054 | | Language | Python | Python | | Adopt for | Awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML. | MLflow is an open-source platform that offers comprehensive capabilities for managing, deploying, and monitoring machine learning models as well as large language models (LLMs) and AI agents. MLflow supports various use, | | Persona | - | - | | Runtime | - | - | | License | - | Apache-2.0 | | Categories | Developer Tools, Evaluation & Observability, Inference & Serving, Model Training | Evaluation & Observability, Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-mlops](/tools/kelvins-awesome-mlops.md) | [mlflow](/tools/mlflow-mlflow.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Very active (96%) | | Days since push | 97d | 0d | | Open issues (now) | 71 | 2.1k | | Stars delta | Unknown | +476 (30d) | | Open issues delta | Unknown | -22 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/kelvins-awesome-mlops/trust.md) | [trust report](/tools/mlflow-mlflow/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: mlflow - **Adopt for:** MLflow is an open-source platform that offers comprehensive capabilities for managing, deploying, and monitoring machine learning models as well as large language models (LLMs) and AI agents. MLflow supports various use, ## Choose when ### Choose awesome-mlops if… - Tags unique to awesome-mlops: ai, awesome, data-science, machine-learning. - Also covers Developer Tools. - You need resources across multiple facets of the machine-learning pipeline, from data validation to model serving. ### Choose mlflow if… - Tags unique to mlflow: agentops, agents, ai-governance, evaluation. - - Use when you're working with a diverse range of environments like local or cloud platforms because MLflow is **vendor-neutral**. - More GitHub stars (28k vs 5.2k) - visibility, not fit. ## 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 mlflow - - Avoid if your organization has strong preferences for proprietary solutions with advanced features not available in the open-source domain. - - Not recommended for users who prefer a fully managed service without self-hosting options, as competitors like Databricks or Azure ML offer integrated services tailored for their cloud environments. ## Common questions ### What is the difference between awesome-mlops and mlflow? awesome-mlops: A curated list of awesome MLOps tools.. mlflow: AI engineering platform for debugging, evaluating, monitoring, and optimizing AI applications. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome-mlops over mlflow? Choose awesome-mlops over mlflow when Tags unique to awesome-mlops: ai, awesome, data-science, machine-learning; Also covers Developer Tools; You need resources across multiple facets of the machine-learning pipeline, from data validation to model serving. ### When should I choose mlflow over awesome-mlops? Choose mlflow over awesome-mlops when Tags unique to mlflow: agentops, agents, ai-governance, evaluation; - Use when you're working with a diverse range of environments like local or cloud platforms because MLflow is **vendor-neutral**; More GitHub stars (28k vs 5.2k) - visibility, not fit. ### 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 mlflow? - Avoid if your organization has strong preferences for proprietary solutions with advanced features not available in the open-source domain. - Not recommended for users who prefer a fully managed service without self-hosting options, as competitors like Databricks or Azure ML offer integrated services tailored for their cloud environments. ### Is awesome-mlops or mlflow more popular on GitHub? mlflow has more GitHub stars (27,591 vs 5,229). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-mlops and mlflow open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to awesome-mlops or mlflow? GraphCanon lists graph-backed alternatives at [awesome-mlops alternatives](/tools/kelvins-awesome-mlops/alternatives) and [mlflow alternatives](/tools/mlflow-mlflow/alternatives) ([awesome-mlops markdown twin](/tools/kelvins-awesome-mlops/alternatives.md), [mlflow markdown twin](/tools/mlflow-mlflow/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-mlflow-mlflow.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, awesome-mlops or mlflow? awesome-mlops: Slowing. mlflow: 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 mlflow? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-mlops trust report](/tools/kelvins-awesome-mlops/trust); [mlflow trust report](/tools/mlflow-mlflow/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/_