Operating a delivery and inference stack means understanding both its behavior and the resources it consumes. These guides connect telemetry to an investigation and costs to a useful application outcome, with explicit boundaries and denominators.
Choose a traffic population and time window, then define exactly what is counted. Separate request cache hits from bytes saved, model responses from validated outcomes, and the first attempt from the complete request path. Consistent definitions make a comparison useful even when the system grows more complex.
For the broader architecture, explore Analytics. Follow the articles below as a reading path, or use the guide directory to connect this topic with the rest of the stack.
A useful AI budget follows each request from the edge to the model and back. Learn to separate billing from operational cost, model retries and caching, and connect infrastructure spending to successful user outcomes.
Useful CDN analytics connects cache behavior, latency, and errors to a clear operational decision. Learn to define trustworthy metrics, interpret request and byte hit ratios, manage sampling and privacy, and test changes with meaningful guardrails.