MLFlow projects are described as

An MLflow Project is a format for packaging data science code in a reusable and reproducible way, based primarily on conventions. In addition, the Projects component includes an API and command-line tools for running projects, making it possible to chain together projects into workflows.

MLFlow Rcipes (previously "pipelines") are described as

MLFlow Recipes is a framework that enables data scientists to quickly develop high-quality models and deploy them to production. Compared to ad-hoc ML workflows, MLflow Recipes offers several major benefits...

They see to serve the same purpose.

• What's the difference?
• When does it make sense to use one over the other?