I will discuss the technical challenges and the techniques used to overcome them when reducing large HST resolved stellar photometry surveys on the Amazon Elastic Compute Cloud (EC2). I will describe the architecture of our photometry pipeline, which we found particularly efficient for reducing the data in multiple ways for different purposes. I will also go over the features of EC2 that make this architecture both efficient to use and challenging to implement. I will further detail how the techniques adopted in the past may be improved or simplified with current EC2 capabilities for those interested in trying such reductions in the future. Finally, I will talk about the challenges of serving the output photometry data products so that they are useful to the community.