How to Track AI Usage and Cost Across Teams
Tracking AI usage cost sounds simple until you try it. Spend is split across vendors, billing owners, and teams. Usage data lives inside each platform. Without a unified view, AI cost optimization is guesswork.
Why team level visibility matters
When AI cost is reported as one big number, you cannot tell which workflows are profitable. Team level breakdowns reveal which functions get real value from AI and which are paying for tools they barely touch.
What to track
- Spend by tool and by team
- Requests, tokens, or active users per team
- Estimated time saved per workflow
- Cost per outcome (per ticket, per draft, per deploy)
How to set this up in a week
1. Centralize spend
Export invoices or pull billing data from each AI vendor. Tag each line item by team or cost center.
2. Pull usage exports
Most major AI platforms expose usage exports. Even monthly totals are enough to start.
3. Load it into an AI ROI dashboard
Midgentic ingests CSVs from any AI tool and produces team level breakdowns of AI usage and cost in minutes. No engineering project required. For a deeper look, read what an AI ROI dashboard is.
Turn tracking into action
Once you can see AI usage cost by team, the next move is obvious: shift seats, retire underperforming tools, and double down on the workflows producing the strongest AI ROI. Pair this with our framework to measure AI ROI and tactics for optimizing AI spend. Start free and get a baseline today.