Model routing is the practice of automatically directing each AI request to the model best suited to handle it. Instead of sending everything to one model, a routing layer looks at the request and chooses: a small, cheap, fast model for easy questions and a larger, more capable model for the hard ones. The user just sees a good answer; the choice happens behind the scenes.
Think of a hospital triage desk. Not every patient needs a specialist surgeon, and sending them all to one would create a queue and a huge bill. Triage sends the sprained ankle to a nurse and the chest pain to a cardiologist. Model routing triages requests the same way, weighing quality against cost per token and response time for each one.
The payoff is real money and speed. Reserving your most powerful LLM for the cases that genuinely need it, while a lighter model handles the rest, can cut costs sharply without users noticing a drop in quality.
There is a catch worth knowing. The router itself adds a step, so a clumsy one can spend more time deciding than it saves, or worse, misjudge a hard question and send it to the weak model. A support bot that routes an angry refund request to a tiny model will produce a confident but wrong answer, which costs more than the tokens you saved. So good routing leans on solid model evaluation and a safe default, escalating to the stronger model whenever it is unsure.
At TopDevs we add model routing when a client’s AI usage grows, so they pay for heavyweight models only on the requests that truly call for them.
