Routing Logic
Routing logic is the rule that decides which part of an AI system handles a request. It sends simple questions to small, cheap models and difficult ones to large, expensive models.
When you type a question into an AI program, it isn’t always the same program component that processes your request. Many providers keep several computing programs ready at the same time: small and fast ones, large and slow ones. So something has to decide where your question goes. This decision rule is called routing logic. The word comes from the English “route”, meaning path or way. Routing logic is thus the switch in the system, and it works invisibly in the background.
Why “Who handles the question?” determines the costs
Large AI models are expensive to operate. Every answer consumes computing time on special graphics chips, and that costs money. A very large model can easily cost ten to thirty times as much per answer as a small one. With millions of requests per day, this factor is decisive for a provider’s profit calculation.
At the same time, the majority of requests don’t need peak performance at all. “How many inhabitants does Hamburg have?” can be reliably answered by a small model. A multi-step math problem or a code analysis cannot. Anyone who sends everything to the largest model burns money. Anyone who sends everything to the smallest one delivers poor answers and loses users.
Routing logic is the attempt to avoid both mistakes. It is therefore a frequent topic in quarterly reports and analyst commentary on AI companies. When a provider reports that costs per answer have fallen, better routing is often behind it rather than a better model.
How the switch makes its decision
In the simplest case, routing logic works with fixed rules. If the request contains an image, it goes to a model that can process images. If it is very long, it goes to a model with a large text memory. Such rules are written by hand by developers, and they are easy to follow.
More sophisticated is learned routing. Here, a small additional model first estimates how difficult the request is. It outputs a kind of difficulty score, and above a certain threshold the request moves to the large model. This additional model is trained with examples where it is known which model was still able to handle the task.
It is important to distinguish this from a related term. In Mixture of Experts, or MoE for short, a router selects building blocks within a single model. Routing logic in the sense meant here selects between entire models or entire services. The idea is similar, but the level is different. Another common misconception is that routing is free: the difficulty estimation itself requires computing time and must not noticeably delay the answer.
Routing in chatbots, search engines, and corporate IT
You see routing most clearly in modern chatbots. Many no longer offer a model selector but decide for themselves whether to answer briefly or “think” longer. This thinking is an expensive mode with many intermediate steps. Whether it is activated is determined by the routing logic. Providers advertise this as convenience, but above all it lowers their costs.
Routing is also embedded in search engines and online shops. There, the system decides whether a request gets a classic results list or a fully formulated AI answer. And in companies, support systems forward customer inquiries either to an automated response or to a human.
This is why news texts feature phrases like “intelligent routing” or “automatic model selection”. They always mean the same principle: a switch between quality and cost. When users complain that a chatbot suddenly answers more superficially than before, this is often not due to the model. Frequently, only the routing logic has been set more strictly.
