How We Cut AI Costs by 40% Without Changing a Single Prompt

Most teams assume prompt optimization is the first move for lowering spend. In practice, model selection usually gives you a larger and faster impact.

We started by grouping requests by workload type, then measured quality thresholds and latency expectations for each group.

After that, we moved non-critical flows to lower-cost models and added guardrails that route high-stakes requests to premium models only when needed.

This hybrid strategy reduced monthly spend by around 40% while preserving user satisfaction scores.