Artificial intelligence 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.
