AI agents

Estimating and attributing token spend

• How to Control LLM Costs in Production
• Estimating Token Spend Before the First Model Call
• How to Attribute Token Spend to Features and Teams
• Allocating AI Spend to Teams and Customers
• Charging Shared Model Spend Back to Teams and Customers
• Cost per Resolved Ticket for Support Agents
• Cost per Scored Run: Budgeting Repeats, Consistency, Model Spend
• Estimating the Cost of a Model Call Before You Make It
• Fields a Gateway Must Log to Make Caps Auditable
• Logging the Overrun Decision: Fields That Prove What Happened
• Making Caps Auditable: Fields the Wrapper Must Log per Call
• One Dashboard for LLM Cost, Latency and Errors
• Output Tokens Are the Variable You Cannot Precompute
• Predicting Token Spend Before a Run Starts
• Reading a Provider's Tokenizer Without Calling the Model
• Reading the Bucket: Which Caller Drained the Pool
• Reproducing a Shipped Failure From Logs: Fields You Need First
• Setting a Tolerance Band on an Output-Length Estimate
• Splitting Shared Model Spend Across Teams and Customers
• Splitting Spend by Caller: From Cap Counts to a Chargeback
• The Counter That Drifts: Testing Cap Math Against the Invoice