Model routing isn't a classification problem—it's a systems optimization challenge: real costs depend on cache hit rates, not just token pricing, and task difficulty is often invisible at routing time.
vLLM proposes embedding multi-model collaboration directly into the inference serving layer, enabling transparent API routing that delivers stable, high-quality outputs at minimal cost.
vLLM Semantic Router introduces Fusion, a routing primitive that lets a panel of models produce independent answers, has a judge model analyze them, and synthesizes a single response — making model composition a first-class serving pattern.