AI Teams Implement Routing Policies and Semantic Caching to Cut Model Costs

Experts warn that simply switching to cheaper AI models does not guarantee cost control. They propose an AI routing policy that formally records which class of work should use a frontier model, a cheaper model, deterministic code, or human review, and assigns ownership. The policy creates a spend‑by‑task record that finance teams can audit, addressing the lack of documented decisions that often obscure AI expenditures.

Another recommended practice is semantic caching, which replaces exact‑match caches with vector‑based similarity searches. By storing query‑response pairs as embeddings, the system can reuse answers for semantically similar requests, lowering token usage and latency. Effective caching depends on tuning similarity thresholds for each use case to avoid incorrect answers while maximizing hit rates.