The IARPA Machine Intelligence from Cortical Networks (MICrONS) program is a research endeavor created to improve neurally-plausible machine-learning algorithms by understanding data representations and learning rules used by the brain through structurally and functionally interrogating a cubic millimeter of mammalian neocortex. This effort requires efficiently storing, visualizing, and processing petabytes of neuroimaging data. The Johns Hopkins University Applied Physics Laboratory (APL) has developed an open-source, highly available service to manage these data, called the Boss. The Boss uses AWS to provide a cloud-native spatial database with an innovative storage hierarchy and auto-scaling capability to balance cost and performance. This system extensively uses serverless components to meet both scalability and cost requirements. In this session, we provide an overview of the Boss, and we focus on how the APL used Amazon DynamoDB, AWS Lambda, and AWS Step Functions for several high-throughput components of the system. We discuss both the challenges and successes with serverless technologies.