Prefect is ideal when you need a robust workflow orchestration tool that can handle dynamic tasks and dependencies at runtime. You would use Prefect over Kedro in scenarios with complex, non-linear workflows where tasks may vary in execution order or where you need real-time monitoring and task retries. For example, if you're developing a machine learning pipeline that adjusts based on incoming data streams and requires dynamic resource allocation, Prefect would be a better fit.
Kedro is ideal to use when you need a structured and reproducible data science project. It helps to maintain clean codebases and facilitates collaboration among teams. For example, if you are working on a data science project where your team follows specific workflows and needs to ensure reproducibility across different stages, Kedro provides the necessary tools to manage data, models, and pipelines effectively.