We agree that in most cases prophet is not a good benchmark; however, we wanted to use it because it is one of the most used libraries in forecasting. For that reason, we also tested the solution against AWS Forecast obtaining better results.
Besides the better performance and scalability, the pipeline we created considering all the stages of time series forecasting: preprocessing (e.g. missing value imputation), creation of static and dynamic features, forecast generation, and finally evaluation using data sets of important competencies. (https://github.com/Nixtla/tsfeatures)
On the deployment side, the entire pipeline can be quickly deployed in the user's cloud using terraform. This allows for less development time. (https://github.com/Nixtla/nixtla)