Argo Workflows

Argo Workflows is the Kubernetes-native workflow engine for when your data pipelines and CI/CD jobs need more than a bash script and a cron job. Define multi-step workflows as Kubernetes resources, with DAGs, parallelism, retries, and artifact passing between steps.

If you're already on Kubernetes and need to orchestrate complex jobs — ML training pipelines, data ETL, batch processing — Argo Workflows fits naturally. Apache Airflow is the bigger ecosystem for data orchestration but runs as its own platform. Tekton is the Kubernetes-native CI/CD alternative. Temporal handles durable execution for application workflows. Commercially, managed Airflow (MWAA, Astronomer) abstracts the ops.

The strength is native Kubernetes integration. Each workflow step runs as a pod, which means you get Kubernetes' scheduling, resource limits, and node affinity for free.

The catch: you need Kubernetes. If you're not already running a cluster, adopting Argo Workflows means adopting Kubernetes, and that's a massive operational overhead for small teams. The YAML configurations get verbose fast, and debugging failed workflows requires comfortable kubectl skills.