Your AI Spend Doesn't Have a Budget. It Should.

Most teams track LLM spend the way they track a utility bill: check it at the end of the month, wince, move on. That works until the bill triples because someone shipped a prompt change that doubled token usage per request.

Cost, performance, and quality are one problem

Cutting spend by swapping to a cheaper model without checking quality just moves the cost somewhere else — into support tickets, re-runs, and lost trust. The teams that get this right treat all three as one connected system:

• Observe — trace usage down to the feature and the prompt version that generated it
• Govern — budgets and guardrails that catch a runaway loop before it becomes a bill, not after
• Optimize — tune prompts, retrieval, and model choice so cost drops without quality slipping

Start with visibility, not a mandate

You can't optimize what you can't see per-request. Before anyone sets a budget or picks a cheaper model, get usage traced to the workflow level. Most of the easy wins — a redundant retrieval call, a prompt that's twice as long as it needs to be — show up in the trace before you ever touch the model.

code review for prompts is still code review. Treat it that way.