We use Kubernetes, according to our IT/devops guys it was pretty straightforward to deploy in Azure with Jupyterhub's KubeSpawner module + documentation. A few people are quite eager to learn Python / code in general, so we try to make it convenient. One common use case would be to work with existing excel spreadsheets, so the notebook volume storage should be mountable in Windows. The file upload/management in notebook servers is quite obtuse IMO.
If a recurring task can be reasonably parameterized then a Streamlit app might be a better choice in some instances. I've developed a monitoring application for our portfolios where I can track daily asset weights, underlying data points, computations etc. Not displaying code ensures that the output can be consumed by a wider audience.
We've tested JH with K8 in GCP which was straightforward also. With a small team though tending towards a single VM deploy (based on "The Littleist JupyterHub") which looks a lot easier to maintain.
If a recurring task can be reasonably parameterized then a Streamlit app might be a better choice in some instances. I've developed a monitoring application for our portfolios where I can track daily asset weights, underlying data points, computations etc. Not displaying code ensures that the output can be consumed by a wider audience.