Model Routing

Model routing is the practice of automatically directing each AI inference request to the most appropriate model from a fleet — balancing cost, latency, and quality — rather than sending all queries to a single model.

Model routing is the practice of automatically directing individual inference requests to different AI models — or model variants — based on criteria such as estimated query complexity, required output quality, latency budget, or cost constraints. Rather than using a single model for all requests, a routing layer selects the best-fit model for each query, optimizing the cost-performance tradeoff across a heterogeneous fleet.

A router sits in front of the model fleet and classifies each incoming query. Simple routers use rule-based heuristics — short queries go to a small model; code-generation requests go to a code-specialized model. More sophisticated routers train a lightweight classifier to predict which model will return an acceptable answer at the lowest cost, using features such as prompt length, topic, or historical user feedback. Cascading systems first attempt a cheaper model and escalate to a larger one only when a quality threshold is not met. Services such as OpenRouter, Martian, and LiteLLM facilitate routing across providers and model families.

The cost gap between frontier and commodity models can be two to three orders of magnitude per token. For a high-volume product handling millions of queries per day, routing even 70–80% of simple or routine queries to a smaller model can reduce inference costs by a factor of 10–50 while maintaining user-visible quality. Routing also enables graceful fallback during provider outages, geographic load balancing, and compliance-driven model selection — for example, routing certain data types to on-premises models.

By 2026, model routing is an established component of enterprise LLM infrastructure. Major cloud platforms — AWS Bedrock, Google Vertex AI, Azure AI Foundry — offer multi-model endpoints with built-in routing hooks. The continued expansion of capable open-weight models such as Llama 3 (Meta), Mistral, Qwen (Alibaba), and DeepSeek has widened the cost-quality Pareto frontier, increasing the practical value of intelligent routing as the performance gap between open and proprietary models has narrowed.