AI Cost Visibility: How to Track and Optimize Token Spend Before the Invoice Arrives

Production AI agents routinely cost far more than pre-launch estimates because retries, context window growth, multi-step reasoning chains, and framework overhead are invisible to simple token-rate math. The post explains why the gap between estimated and actual LLM spend can be 7-10x, notes that GitHub Copilot's shift to usage-based billing in June 2026 signals an industry-wide trend, and outlines a trace-level observability workflow to catch cost spikes before invoices arrive. It also warns that observability tooling itself can become an uncontrolled cost (illustrated by an Azure AI Foundry case where default-enabled evaluations silently billed users). The bulk of the post demonstrates Progress Observability, a Telerik product, showing Python and .NET SDK instrumentation with decorators (@agent, @workflow, @task, @tool) that produce per-span token counts and cost attribution, a Cost Analytics Dashboard, and a tag-based system for slicing spend by customer, experiment, or release version.