The AI landscape is shifting from model-centric development to infrastructure challenges. As generative AI applications move from experimentation to production, organizations face complex issues around cost, governance, and operational chaos driven by model fragmentation and multi-provider environments.

Early adoption in late 2022 focused on "pet projects." By 2024, a "great culling" eliminated most non-viable projects, with remaining applications entering production in 2025. This transition highlights the need for reliability, auditability, and cost control at scale. The core problem isn't model capability, which has advanced rapidly, but the lack of robust infrastructure to manage diverse models, APIs, pricing, and latency profiles across different providers.

New tools like Otari aim to provide this missing "control layer." Otari, built by Mozilla, offers an open-source control plane for LLMs, designed to give organizations agency over their AI infrastructure. It focuses on intelligent routing by cost, capability, and compliance, real-time observability, and policy enforcement, enabling AI operations as a mature engineering discipline. This infrastructure focus is becoming the new competitive moat as model advantages diminish.