Production AI systems rarely depend on a single model. Different workloads may require low latency, specialized reasoning, long context windows, multimodal processing, or strict data-handling policies. Real-time model routing provides the decision layer that maps each request to the most appropriate inference endpoint.
At scale, this layer must operate as an independent control plane. Application services send normalized requests to an inference gateway, while the router evaluates model availability, request characteristics, policy constraints, and current performance. Separating routing logic from application code makes the infrastructure easier to update without redeploying every client.
Platforms such as ModelRouter AI illustrate this architectural pattern by centralizing access to models behind a consistent routing interface. The objective is not merely e