Increasingly large datasets are being made publically available yet the methods and modelling skills to analyze them are lagging behind. In an effort to overcome barriers to analyzing and modelling data we propose the paradigm of Modelling as a Service (MaaS). As a proof-of-concept, we present a case study of the MaaS paradigm with computational tools for optogenetics based upon PyRhO. We demonstrate the benefits it confers in terms of enhanced scope for collaboration, reproducibility and ease of use, especially for scientists with a limited computational background, and discuss directions for future growth. Eventually we aim to grow this project in scope to encompass other modelling and analysis tools, and migrate to JupyterHub for persistent individual user accounts and storage. In the meantime, we hope that this approach will serve to demonstrate how MaaS can substantially increase the appeal and accessibility of modelling.