Prefect is often preferred for data workflows that require dynamic decision-making based on runtime data or when tasks need to change behavior during execution. For example, if you have a workflow that involves processing batches of data where the number or type of tasks can change based on the content of the data (like different ETL operations based on data quality checks), Prefect's dynamic task creation and management would be more beneficial than Argo's static workflow definitions.
Argo Workflows is ideal when you need to run large-scale batch jobs in a Kubernetes environment. It leverages Kubernetes for resource management, allowing you to scale easily and utilize the cloud-native features. If your workflows require conditional execution and looping constructs, Argo can handle these with minimal configuration. A specific scenario would be deploying machine learning training jobs that must scale dynamically based on data volume and resource availability in a Kubernetes cluster.