Analysis of large neuroimaging datasets requires scalable computing power and storage, plus methods for secure collaboration and for reproducibility. The application of cloud computing can address many of these requirements, providing a very flexible model that is generally far less expensive than a lab trying to purchase the most computer equipment they would ever need. This chapter describes how researchers can change the way that they traditionally run neuroimaging workflows in order to leverage cloud-computing capabilities. It describes various considerations and options related to cloud-based neuroimaging analyses, including cost models and architectures. Next, using data from the AOMIC-PIOP2 project hosted on OpenNEURO, it shows how to use Nextflow to create a very simple skull stripping and tissue segmentation workflow using FSL’s bet and fast programs installed on a local computer. Nextflow allows scalability from a laptop to a cluster to cloud-native services with no code changes.