The authors examine how platform companies structure labor control in the food-delivery sector by comparing two major apps operating in the United States and Canada: Uber Eats and Fantuan. Although existing research on algorithmic management emphasizes the emergence of âalgorithmic despotismââopaque, data-driven systems that shape worker behavior through surveillance, nudges, and individualized pay schemesâthere is a need to map out the variations in platform control. Drawing on a yearlong ethnography that combined app walk-throughs, participant observation, and semistructured interviews with 34 delivery workers in the greater Toronto/Hamilton area, the authors analyze the key organizational ânodesâ of each platform, from onboarding and order dissemination to pay structures and worker surveillance. Our comparative analysis demonstrates that labor control under platform capitalism does not operate as a single, monolithic system. Uber Eats deploys a âdispersive-extractiveâ model that extracts profit through algorithmic opaqueness and performance metrics throughout the driverâs workflow. Fantuan, by contrast, operates through an âethnoculturalâ model in which ethnocultural norms as well as human managerial interventions work alongside algorithmic systems to enforce hierarchy, efficiency, and compliance. By mapping these distinct regimes, the authors develop a comparative framework for understanding the variations within algorithmic management and calls for more research across ethnically diverse platform markets.