Model routing is the infrastructure layer that directs AI requests to the optimal model based on task complexity, cost constraints, and latency requirements. Instead of routing every request to the most expensive model, a routing engine classifies tasks and matches them to the most cost-effective model that meets quality requirements.
Key Concepts
01Task classification — categorize incoming requests by complexity (simple lookup vs. complex reasoning)
02Cost-quality tradeoff — route simple tasks to cheaper models, reserve expensive models for complex tasks
03Latency routing — select models based on response time requirements for real-time vs. batch workloads
04Fallback chains — automatic failover to alternative models when primary model is unavailable or degraded
05Governance integration — routing decisions logged for audit trails, cost attribution per route