FinOps for AI workloads
Control LLM and inference costs: measure cost per task, route to the right model, cache sensibly and set budgets without throttling the product.
Measure cost per task, not just the monthly bill
A monthly invoice tells you that spend rose, not why. Attribute tokens, GPU time and storage to features and tenants, then divide by completed tasks. Cost per resolved ticket or per processed document is something a product owner can reason about.
- Tag every request with feature, tenant and model.
- Track retries and failed calls — they cost money and deliver nothing.
- Report cost alongside a quality metric for the same feature.
Pick the right lever for the cost driver
Different drivers need different fixes. Validate each change against your evaluation set before rolling it out.
| Lever | When it helps | What to check |
|---|---|---|
| Model routing | Many requests are simple enough for a smaller model | Quality on the hard cases that still need the larger model. |
| Caching | Repeated questions or shared context | Staleness and permission boundaries between users. |
| Context trimming | Prompts carry long, rarely used context | Answers still cite the evidence they need. |
| Batch processing | Work does not need an immediate answer | Acceptable delay for the business process. |
Set budgets and guardrails that fail gracefully
Budgets work best with warning levels, per-tenant limits and a defined degraded mode — for example a smaller model or a queued response — instead of a hard stop that breaks the product. Review provider pricing regularly, since it changes.
Working on something similar?
Discuss your workflow, available data and operating constraints with Talee’s engineers to assess a suitable next step.
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