This study explores the synergies between two complementary interventions for countering online extremism: deplatforming, a “hard” intervention that involves removing extremist users from online platforms, and inoculation, a “soft” intervention that exposes users to weakened forms of extremist arguments to build resistance. We developed an agent-based model to simulate the diffusion and control of online extremism, using data from systematic reviews and empirical studies. The model focuses on a population of Bayesian agents within an anti-immigration Facebook group, where these agents form beliefs about far-right content spread by extremists. We introduce both deplatforming and inoculation as interventions in the model to control the spread of extremism and find strong evidence that inoculation has a synergistic effect on deplatforming. This synergy stems from inoculation’s ability to stabilise opinion dynamics at the group level, leading to more effective containment of extremist beliefs. The findings suggest that a combined approach involving both deplatforming and inoculation is more effective than implementing either intervention in isolation. We deploy a cost–benefit analysis to show that the existence of synergies at a structural level enables the identification of efficient resource allocation. Our article demonstrates the potential of applying agent-based modelling to the study of counter-extremism policies.