AI model routing places a decision layer between an application and multiple available models. The router examines information such as task type, complexity, sensitivity, expected quality, latency target, and budget, then chooses an appropriate model or deployment path.
A routing policy can use fixed rules, learned classifiers, confidence estimates, or staged fallbacks. Effective routing reduces unnecessary use of expensive frontier systems while preserving access to them for requests that genuinely benefit from greater capability.
ELI5
Model routing chooses which AI model should handle each request. Instead of sending every job to the largest or cheapest model, a routing layer considers what the task needs and selects a suitable option.
For example, a short spelling correction can go to a fast low-cost model, while a difficult software investigation can go to a stronger model. Sensitive requests may stay on a local model even when a cloud model is more capable.
Why use an AI model router?
A router can improve cost and responsiveness by sending routine work to efficient models and difficult work to more capable models.
Does AI model routing require more than one provider?
No. Routing can choose among models from one provider, several providers, or a combination of hosted and self-managed deployments.
