In this dissertation, I describe the design, theory, and implementation of performant crowd-powered systems. After discussing the performance implications of involving humans in data analysis workflows, I present an example of a data cleaning system that requires low-latency crowd input. Then, I describe CLAMShell, a system that accurately labels large-scale datasets in one to two minutes, and its evaluation on over a thousand workers processing nearly a quarter million tasks. Next, I consider the design of multi-tenant crowd systems running many heterogeneous applications at once. I describe Cioppino, a system designed to improve throughput and reduce cost in this setting, while taking into account worker preferences. Finally, I explore the theory of identifying fast individuals in an unknown population of workers, which can be modeled as an instance of the infinite-armed bandit problem. The analysis results in novel near-optimal algorithms with applications to broader statistical theory. Together, these components provide for the implementation of human computation systems that are cost-efficient, scalable, and fast enough to integrate into existing data analysis workflows without compromising performance.