Where the money leaks
The usual culprits: using an expensive model for simple tasks, sending more context than the task needs, repeating calls that could be cached, and workflows that call the model more often than necessary.
Match the model to the task
Route each task to the cheapest model that meets the quality bar. Reserve large models for the work that needs them. A clear routing policy is often the single biggest saving.
Trim context and cache
Send only the context the task requires. Cache stable results so you do not pay to regenerate the same answer. Small changes here add up across volume.
Fix the workflow, not just the prompt
Sometimes the cheapest move is upstream: fewer steps, better inputs, or not using AI for a step at all.
When this matters
- Your AI bill is rising faster than the value.
- You have no cost model for AI spend.
- Usage is scaling and costs are scaling with it.
What to avoid
- Cutting quality to save money instead of cutting waste.
- Optimizing prompts while ignoring model choice and routing.
- Adding caching without checking the results are safe to reuse.
- Treating the bill as fixed instead of measurable.