Token tracking is becoming a core engineering metric

For years, we treated infra costs as engineering data and AI costs as “finance later.”
That no longer works.

Token usage now affects:

• Feature pricing
• Gross margin
• Prompt architecture
• Model selection strategy

So we started treating token metrics like first-class engineering telemetry.

What changed after adopting tokenusage.site

• PMs could see usage impact by release
• Engineers could compare implementation choices with cost context
• Leadership got cleaner forecasting instead of guesswork

When AI is part of your product, token behavior is not a billing detail.
It’s product performance data.

Curious how others are handling this in production.

https://tokenusage.site