Apache Airflow vs Argo Workflows
A side-by-side comparison of two Data Pipeline AI agents — to help you pick the right one.
Apache Airflow
Apache Airflow is an open-source platform for programmatically authoring, scheduling, and monitoring workflows. It uses directed acyclic graphs (DAGs) to define tasks and dependencies, with a built-in scheduler and web-based UI for visualization and management.
Argo Workflows
Argo Workflows is a Kubernetes-native workflow engine designed to orchestrate parallel, containerized jobs. It enables users to define, schedule, and execute multi-step workflows as Kubernetes custom resources, simplifying complex data processing and machine learning pipelines.
| Apache Airflow | Argo Workflows | |
|---|---|---|
| Category | Data Pipeline | Data Pipeline |
| Open source | Yes | Yes |
| Self-hostable | Yes | Yes |
| Skill level | Intermediate | Intermediate |
| Pricing | Open Source | Open Source |
Apache Airflow: what it solves
It automates and orchestrates complex data pipelines, ensuring tasks execute in the correct order and recover from failures.
Argo Workflows: what it solves
It automates and manages the execution of containerized workflows on Kubernetes, eliminating manual orchestration and scaling challenges for distributed tasks.