Chatbot service providers (e.g., OpenAI) rely on tiered subscription plans to generate revenue, offering black-box access to basic models for free users and advanced models to paying subscribers.However, this approach is unprofitable and inflexible.A pay-to-unlock scheme for premium features (e.g., math, coding) offers a more sustainable alternative.Enabling such a scheme requires a feature-locking technique (FLoTE) that is (i) effective in refusing locked features, (ii) utility-preserving for unlocked features, (iii) robust against evasion or unauthorized credential sharing, and (iv) scalable to multiple features and clients.Existing FLoTEs (e.g., password-locked models) fail to meet these criteria.To fill this gap, we present LOCKET, a more robust and scalable FLoTE to enable pay-to-unlock schemes.We develop a framework for adversarial training and merging of feature-locking adapters, which enables LOCKET to selectively disable specific features of a model.Evaluation shows that LOCKET is effective (100% refusal rate), utility-preserving (≤ 7% utility degradation), robust (≤ 5% attack success rate), and scalable to multiple features and clients.