LLM Routing Is Becoming the Hidden Product Layer in AI Apps

LLM Routing Is Becoming the Hidden Product Layer in AI Apps For the first wave of generative AI products, the model choice was easy to understand. A company picked a frontier LLM, wrapped a chat interface around it, added a few prompts, and shipped. The product pitch often sounded like the model pitch: smarter, longer context, better reasoning, more fluent answers. That era is ending. The most competitive AI applications are no longer built around one model. They are built around a routing layer that decides which model should handle each request. This shift is subtle because users rarely see it. They still type into a box, click a button, or trigger an AI agent in the background. But behind the interface, the application may choose between a frontier model, a small language model, a vision model, a code-specialized model, an embedding model, a local model, or a cheaper fallback. In practice, LLM routing is becoming one of the most important product layers in AI. The one-model app is becoming too expensive A single premium LLM can make a demo look impressive. It is less attractive when the product has real users, repeated workflows, and narrow margins. Not every task needs t