Model transformations are widely used by Model-Driven Engineering (MDE) platforms to apply different kinds of operations over models, such as model translation, evolution or composition. However, existing solutions are not designed to handle very large models (VLMs), thus facing scalability issues. Coupling MDE with cloud-based platforms may help solving these issues. Since cloud-based platforms are relatively new, researchers still need to investigate if/how/when MDE solutions can benefit from them. In this paper, we investigate the problem of transforming VLMs in the Cloud by addressing the two phases of 1) model storage and 2) model transformation execution in the Cloud. For both aspects we identify a set of research questions, possible solutions and probable challenges researchers may face.