FinOps transformed how companies manage cloud spending. The same principles — visibility, optimization, and governance — apply directly to AI agent costs. But the implementation looks different.
Phase 1: Visibility (Inform)
In cloud FinOps, this means tagging resources and building cost dashboards. For AI agents, it means:
- Per-agent cost attribution — Know which agent spends what
- Per-model breakdowns — Understand your model mix and pricing tiers
- Per-task economics — Calculate the cost of business outcomes, not just API calls
- Real-time dashboards — Don't wait for end-of-month bills
This is where AgentBurn fits. It's the cost visibility layer for AI infrastructure, the same way CloudHealth or Vantage is for AWS/GCP.
Phase 2: Optimization (Optimize)
Once you can see costs, you can reduce them:
- Model routing — Send simple tasks to cheap models (GPT-4o-mini, Haiku) and complex tasks to expensive ones (GPT-4o, Opus)
- Prompt compression — Shorter prompts mean fewer tokens
- Caching — Cache responses for identical or similar queries
- Batch processing — Some providers offer 50% discounts for async batch API calls
- Context window management — Summarize long conversations instead of sending full history
Phase 3: Governance (Operate)
Sustainable cost management requires organizational processes:
- Budget allocation — Assign budgets per team, project, and agent
- Alert policies — Automated notifications when spend crosses thresholds
- Cost review cadence — Weekly reviews of cost dashboards with engineering leads
- Approval gates — Require sign-off for new agents or model upgrades that increase costs
Teams that treat AI spend like cloud spend — with visibility, optimization, and governance — are well-positioned to spend significantly less than teams that fly blind.