Your AI spend doubled last quarter and nobody can explain why. Redundant API calls, oversized models on simple tasks, prompts burning tokens nobody reviewed. We audit the waste and cut it. Without degrading output quality.
AI costs follow the same trajectory as early cloud costs: they start small, grow exponentially, and become a line item nobody can justify by the time finance asks questions. The difference is attribution. A cloud instance has a clear owner. An API call to GPT-4 that could have been handled by a model one-tenth the size does not show up as waste on any dashboard. It just shows up as a higher bill.
We audit with the specificity of a cost accountant. Every API call traced to a feature. Every model endpoint evaluated against cheaper alternatives. Every prompt analyzed for token efficiency. We routinely find prompts that achieve identical output quality at 30-50% fewer tokens by restructuring the instruction or switching from few-shot to zero-shot where the task allows it.
Then we cut.
Model routing that sends classification tasks to smaller, cheaper models and reserves large models for complex reasoning. Semantic caching that eliminates redundant computation. Batch processing where real-time response is unnecessary. Prompt compression that reduces context length without information loss. Each optimization measured against the quality baseline. Nothing degrades.
Companies reduce AI spend by 40-60% through these interventions with no measurable change in output quality. The waste is structural, not intentional. It accumulates because nobody was looking at cost per query when the system was built. We make it visible. Then we eliminate it.
Related Reading
6 articles
AI spend growing faster than AI value? Run the numbers.