Over recent years, online labor platforms (OLPs) such as Uber or Deliveroo have become increasingly popular. One key facilitator of OLP’s success is their usage of algorithmic control (AC), where managerial control is performed by algorithmic technologies. With AC, OLPs can exert tight control over workers, which triggers workers’ motivation to deploy algoactivistic practices (i.e., resistance practices against AC). This study examines how different AC mechanisms influence workers’ individual-level algoactivistic practices. We conducted a grounded theory approach on 22 interviews with Uber drivers, where we found how drivers perform proactive algoactivistic practices against algorithmic monitoring, active algoactivistic practices against algorithmic suggesting, and reactive algoactivistic practices against algorithmic imposing. Overall, we developed a theoretical model that explains how the shifting intervention level among different AC mechanisms impacts workers’ latitude to deploy algoactivistic practices. Therefore, we contribute to a nuanced understanding of workers’ agency to contest their working conditions on OLPs.