Airflow is ideal when you need a mature tool with broad use cases, especially in data engineering tasks, where complex dependencies and scheduling are crucial. For example, if you're orchestrating a daily ETL (Extract, Transform, Load) pipeline that involves numerous data sources and requires fine-grained control over task execution order, Airflow provides the necessary features and ease of use for such scenarios.
Use Argo Workflows when you are already operating in a Kubernetes environment and need to orchestrate microservices or batch jobs directly on Kubernetes. It's especially beneficial when your workloads can leverage the features of Kubernetes directly, like running tasks in parallel or using specialized Kubernetes resources, such as GPUs for data processing or machine learning tasks. For example, if you're building a machine learning pipeline that runs data preprocessing, model training, and evaluation across multiple containers managed by Kubernetes, Argo Workflows would provide a more streamlined approach.