Enterprise AI inference spend is growing faster than the value it delivers. Most organizations route every request to the most expensive frontier model regardless of task complexity. An intelligent routing layer matches each request to the right-sized model, cutting inference costs 60–80% with no measurable quality degradation on the majority of workloads.
The default enterprise AI architecture is expensive by design. Every query routes to GPT-4o, Claude Opus, or Gemini Ultra — frontier models priced for their ceiling capability, not their average task. A customer service query asking for a return policy, a document classification task, a simple summarization request: all priced at the same rate as complex multi-step reasoning. At enterprise scale, this is a systematic overallocation of compute spend that compounds with every additional use case deployed.
Model routing research (FrugalGPT, 2023; RouteLLM, 2024) demonstrates that 70–85% of enterprise LLM queries can be served by smaller, cheaper models with output quality indistinguishable from frontier models on the specific task. An inference optimization layer sits between the application and the model APIs, classifies each query by complexity and required capability, routes to the lowest-cost model that meets the quality bar, and falls back to frontier models only when necessary. The routing decision adds less than 5ms of latency and pays for itself in the first week of production traffic.
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