--- title: "airflow vs awesome-mlops" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/apache-airflow-vs-kelvins-awesome-mlops" tools: ["apache-airflow", "kelvins-awesome-mlops"] --- # airflow vs awesome-mlops *GraphCanon updated Sep 20, 2026* ## Verdict Pick airflow if apache Airflow is a Python-based orchestrator for scheduling and monitoring workflows, suitable for tasks that require flexible DAG (Directed Acyclic Graph) definitions; pick awesome-mlops if awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML. [airflow](https://airflow.apache.org/) reports 47k GitHub stars, 18k forks, and 2.1k open issues, last pushed Sep 14, 2026. [awesome-mlops](https://github.com/kelvins/awesome-mlops) has 5.3k stars, 775 forks, and 82 open issues, last pushed Aug 17, 2026. Figures are from public GitHub metadata via [airflow's repository](https://github.com/apache/airflow) and [awesome-mlops's repository](https://github.com/kelvins/awesome-mlops). | | [airflow](/tools/apache-airflow.md) | [awesome-mlops](/tools/kelvins-awesome-mlops.md) | | --- | --- | --- | | Tagline | A platform to programmatically author, schedule, and monitor workflows | A curated list of awesome MLOps tools. | | Stars | 46,844 | 5,265 | | Forks | 17,829 | 775 | | Open issues | 2,144 | 82 | | Language | Python | Python | | Adopt for | Apache Airflow is a Python-based orchestrator for scheduling and monitoring workflows, suitable for tasks that require flexible DAG (Directed Acyclic Graph) definitions. | Awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | - | | Categories | Developer Tools | Developer Tools, Evaluation & Observability, Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [airflow](/tools/apache-airflow.md) | [awesome-mlops](/tools/kelvins-awesome-mlops.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 0d | 18d | | Open issues (now) | 2.1k | 82 | | Stars delta | +419 (30d) | +36 (30d) | | Open issues delta | +262 (30d) | +11 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/apache-airflow/trust.md) | [trust report](/tools/kelvins-awesome-mlops/trust.md) | ## Shared compatibility - **Python**: [airflow](/tools/apache-airflow.md) - Python runtime; [awesome-mlops](/tools/kelvins-awesome-mlops.md) - Python runtime ## Decision facts: airflow - **Adopt for:** Apache Airflow is a Python-based orchestrator for scheduling and monitoring workflows, suitable for tasks that require flexible DAG (Directed Acyclic Graph) definitions. ## Decision facts: awesome-mlops - **Adopt for:** Awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML. ## Choose when ### Choose airflow if… - Tags unique to airflow: airflow, apache, automation, dag. - airflow ships Docker support for self-hosted deployment. - If you need to model complex workflow dependency graphs with Directed Acyclic Graphs (DAGs). ### Choose awesome-mlops if… - Tags unique to awesome-mlops: ai, awesome, data-science, machine-learning. - Also covers Evaluation & Observability, Inference & Serving, Model Training. - You need resources across multiple facets of the machine-learning pipeline, from data validation to model serving. ## When NOT to use airflow - Avoid if you require Windows as the primary execution environment without using WSL2. - If your project strictly adheres to MariaDB for database management, Airflow is not recommended because it is neither tested nor supported by the tool. ## 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. ## Common questions ### What is the difference between airflow and awesome-mlops? airflow: A platform to programmatically author, schedule, and monitor workflows. awesome-mlops: A curated list of awesome MLOps tools.. See the comparison table for live GitHub stats and shared categories. ### When should I choose airflow over awesome-mlops? Choose airflow over awesome-mlops when Tags unique to airflow: airflow, apache, automation, dag; airflow ships Docker support for self-hosted deployment; If you need to model complex workflow dependency graphs with Directed Acyclic Graphs (DAGs). ### When should I choose awesome-mlops over airflow? Choose awesome-mlops over airflow when Tags unique to awesome-mlops: ai, awesome, data-science, machine-learning; Also covers Evaluation & Observability, Inference & Serving, Model Training; You need resources across multiple facets of the machine-learning pipeline, from data validation to model serving. ### When should I avoid airflow? Avoid if you require Windows as the primary execution environment without using WSL2. If your project strictly adheres to MariaDB for database management, Airflow is not recommended because it is neither tested nor supported by the tool. ### 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. ### Is airflow or awesome-mlops more popular on GitHub? airflow has more GitHub stars (46,844 vs 5,265). Stars measure visibility, not whether either tool fits your constraints. ### Are airflow and awesome-mlops open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to airflow or awesome-mlops? GraphCanon lists graph-backed alternatives at [airflow alternatives](/tools/apache-airflow/alternatives) and [awesome-mlops alternatives](/tools/kelvins-awesome-mlops/alternatives) ([airflow markdown twin](/tools/apache-airflow/alternatives.md), [awesome-mlops markdown twin](/tools/kelvins-awesome-mlops/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/apache-airflow-vs-kelvins-awesome-mlops.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, airflow or awesome-mlops? airflow: Very active. awesome-mlops: 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 airflow and awesome-mlops? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [airflow trust report](/tools/apache-airflow/trust); [awesome-mlops trust report](/tools/kelvins-awesome-mlops/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=apache-airflow`](/api/graphcanon/graph?tool=apache-airflow) - 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/_