ChatGPT SearchOct 12, 2026
To monitor token burn rate across all your internal AI apps, I’d build a centralized AI observability dashboard that combines token usage, spend, throughput, and budget forecasts across every model provider and application.
The key is to measure more than total tokens: you want to know which apps are burning tokens fastest, what that burn costs, whether usage is accelerating, and when a team or application will hit its budget.
What your dashboard should look like
AI Usage Control Center
All internal apps · All model providers
(Live-ready design)
Tokens consumed
—
Input + output tokens
Current burn rate
—
Tokens per minute
Estimated spend
—
Today / this month
Projected monthly spend
—
Based on observed usage
Token burn over time
Illustrative chart layout — connect your telemetry to populate actual values.
genui{"charts_widget_v2":{"content":{"chartType":"line","meta":{"footer":"Example shape only; not actual usage data."},"xKey":"time","series":[{"dataKey":"input","label":"Input tokens/min","valueFormat":"compact"},{"dataKey":"output","label":"Output tokens/min","valueFormat":"compact"}],"data":[{"time":"09:00","input":3200,"output":900},{"time":"09:10","input":4100,"output":1300},{"time":"09:20","input":3700,"output":1100},{"time":"09:30","input":5600,"output":1800},{"time":"09:40","input":4900,"output":1600},{"time":"09:50","input":6800,"output":2200}]}}}
Usage by application
Internal assistant
Example
RAG pipeline
Example
