LLM Feature-Level Access Control

Companies deploying LLMs want to restrict access to specific features (coding, customer support, etc.) based on user tier or permissions. Locket provides feature-level access control for LLMs, enabling A/B testing, pay-to-unlock monetization, and content/age restrictions without rebuilding the model.

Enterprise AI governance is a real and growing procurement requirement — compliance teams at regulated companies are actively asking how to scope what an LLM can do per user role, and that pressure is accelerating as internal AI deployments mature past the proof-of-concept stage. No clear incumbent owns this exact layer; AuthKit and similar auth tools handle identity, and LLM gateways like Portkey or Kong AI handle routing, but fine-grained feature-level permissioning sits in the gap between them. The $2k–$10k/mo revenue band is plausible for a devtools middleware play but requires landing mid-market or enterprise contracts quickly, since developer-tier pricing alone won't get there. The biggest risk is that this is a thin abstraction — most engineering teams will convince themselves they can implement role-based prompt routing in an afternoon, making paid adoption a hard sell unless the product goes deep on auditability, compliance reporting, or integrations that genuinely take months to build in-house.