Inference routing efficiently directs AI requests to the best-fitting model based on varied criteria. This intelligent routing surpasses simple load balancing by considering factors like request complexity and resource availability. Inference routers, like vLLM and OpenRouter, optimize performance within multi-model environments. The core goal is to ensure each request is handled by the most suitable model available. The selection process begins by determining the request type and use case. Evaluating models involves benchmarking against metrics like accuracy, latency, and cost. Tools like Azure AI Foundry provide model benchmarks for evaluating performance and suitability. Standardized tests enable detailed comparison of candidate models. This comparison allows developers to identify trade-offs. The focus is on finding the optimal balance between performance and efficiency. Ultimately, inference routing improves the overall effectiveness of AI applications.