Tracking LLM cost per user in production is still hard
Teams running in production need practical ways to handle and cost tracking. The main questions are which tools teams use, what still breaks in real use, and how costs can be assigned to each request or user as traffic grows.
Total spending is not enough when usage scales, because teams need to know which features, workflows, or users are driving the bill. The focus is early research into real production , not a product pitch.
Key points
- Production LLM teams are looking for better and cost tracking.
- The hard part is assigning cost to each request or user as traffic grows.
- Total monthly spend does not show which workflows are expensive.
- AI agent teams need this data before they can cut token use or redesign costly flows.