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Why AI Cost Varies

Two SaaS companies with similar AI features can post AI cost ratios that differ by an order of magnitude. The variance is structural, not accidental, and traces back to five decisions.

Decision 1: model defaulting

Teams that default to the flagship model for every call pay 5–20× more than teams that route by intent. This is the single largest source of variance.

Decision 2: prompt discipline

Bloated system prompts, unbounded chat history, and verbose tool schemas compound on every call. Disciplined teams run 40–60% lower input volume.

Decision 3: caching adoption

Teams that wire prompt caching cut input cost 50–90%. Teams that ignore it pay full price every call — a structural gap, not a small optimization.

Decision 4: observability

Without per-feature attribution, runaway loops and dev traffic hide in the bill. Teams with real-time cost dashboards catch anomalies within an hour.

Decision 5: org accountability

When AI cost has an owner (FinOps, platform, or a named engineer), it stays controlled. When it is "everyone's problem", it grows unchecked.

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