Cost & FinOps
AI costs can scale unexpectedly — a prompt that works in development can become expensive at production volume. This section covers how AI costs are structured, the controls that prevent runaway spend, when batch processing dramatically reduces costs, and how to build dashboards that give finance, engineering, and product the visibility they each need.
In This Section
Token & Cost Drivers
What drives AI API costs — input tokens, output tokens, model tier, context length — and how to identify the biggest cost levers in your system.
Cost Guardrails
Hard and soft spending limits, per-user and per-use-case budgets, and real-time controls that stop runaway cost before it hits your bill.
Batch vs Realtime
When asynchronous batch processing cuts costs by 50% — the decision framework and design patterns for batch AI workflows.
Measurement Dashboards
Building AI cost dashboards for executive, engineering, and product audiences — what each view needs and how to build it from a shared data pipeline.