This dissertation examines how algorithmic metrics become sources of labor control and resistance in the gig economy. Using a qualitative multi-case study approach, this dissertation investigates (a) how labor platforms use metrics to govern their distributed workforces and (b) how digitally-enabled service workers encounter, interpret, and manage metrics as part of their daily work practices and service interactions. Drawing on the discursive analysis of platforms’ corporate discourses and in-depth interviews with 50 workers in the United States, this project examines and compares workers’ practices across three kinds of labor platforms, namely, ride-hailing platforms, TaskRabbit, and delivery platforms. These platforms are built around algorithmic metrics that entail constant monitoring and surveillance of workers’ performance, while platform-based labor processes and the visibility of metrics vary across the three cases. I argue that metrics, particularly customer ratings, rationalize and reinforce the management of workers by customers through the production of work-related uncertainty and anxiety. Meanwhile, I find that workers learn to strategically manage their customers before, during, and after service interactions to reduce work-related uncertainty, and thus maintain their autonomy. I also discuss how worker-to-worker online communities emerge as important social spaces for workers to share strategies and feel connected. Furthermore, I explore how socio-technical features of labor platforms and workers’ economic dependence on the platform shape the disciplinary outcomes of metrics. Taken together, this dissertation offers a comparative lens for understanding the role of algorithmic metrics in shaping the trilateral relationship between platform owners, service workers, and customers in the gig economy. It also underscores the need to rethink how metrics, and more broadly, digital data transform the service triangle in workplaces.