Scale vs. Spend: How are you actually tracking and cutting production AI costs?

Hey everyone, We all know running AI at scale is expensive, but the real pain starts when usage spikes and you have no idea why. I’m doing some early-stage research (not selling anything, I promise) on the raw economics of production AI. I want to hear about the ugly realities and engineering scars, not the ideal textbook solutions. If you are running AI in production, I’d love your take on any of these: The Bill: How do you track unit costs (per request, task, or customer)? The Spike: What broke the last time your inference costs suddenly went vertical? The Root Cause: How long did it take you to find why it spiked? The Fix: What did you change to cut costs (caching, routing, smaller models, prompt trimming)? The Trade-off: How did you prove the cost-cut didn't ruin quality or latency? The Stack: Did you build internal tracking tools, or are you stuck with manual spreadsheets? The Owner: Who actually gets blamed when the API bill arrives? If you've spent weeks debugging a massive OpenAI or Anthropic bill, what did you learn the hard way? Thanks!