Model Routing Is Becoming the Control Plane for Enterprise AI

Enterprise AI teams used to ask a simple question: which large language model should we use? That question is starting to look outdated. In production, the better question is becoming: which model should handle this request, under these conditions, for this user, at this price, with this risk profile? That shift is turning model routing into one of the most important layers in the AI stack. It is not as flashy as a new frontier model release, and it is not as easy to market as a chatbot demo. But for companies running AI at scale, routing may become the control plane that determines whether LLM deployments are affordable, reliable, and compliant. The single-model era was always temporary Many early enterprise AI deployments were built around a default model. A team chose a leading API, connected it to an internal tool, wrote prompts, added guardrails, and shipped. That approach worked when use cases were experimental and traffic was small. But production AI rarely stays simple. A support assistant may need a fast, cheap model for classifying tickets, a stronger reasoning model for complex refund disputes, an embedding model for retrieval, and a vision model for uploaded scree