This paper argues that as capable AI models proliferate and prices fall, competitive value moves from owning a model to controlling how requests are routed. A routing layer can choose models based on cost, latency, quality, jurisdiction, data sensitivity, customer tier, SLA, budget, and availability while logging each decision for governance and auditability. Routing operates at three levels: GPU selection within clusters, traffic placement across clusters, and policy-driven model selection. It can provide automatic failover and translate business objectives into model choices without changing application APIs. PaletteAI combines llm-d, LiteLLM, and intelligent model routing across local and external models. The maturity model progresses from hard-wired models to load balancing, policy rou

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