The Routing Stack: When Cascade Models Beat One-Size-Fits-All Inference

Most production teams route every large language model (LLM) call through one flagship model because it simplifies eval and avoids "wrong tier" incidents. That default is expensive. A composite workload—support triage, draft generation, and legal escalation—does not need the same inference profile on every request. By implementing a tiered routing stack, a composite operator reduced inference spend by 34%—saving over $16,000 per month—with no measurable drop in quality scores on its highest-volume workflows.

A routing stack beats one-size-fits-all when routing is observable and tied to $/decision. A small classifier or rules layer sends easy requests to a mid-tier model, escalates ambiguous cases to a flagship, and logs every branch. Finance sees which tier paid for which outcome—not a single blended rate hiding 40% overspend on trivial calls.