The FinOps Guide to AI Spending: How to Track and Control LLM Costs

Why AI Costs Are Hard to Control

Traditional SaaS infrastructure costs scale predictably — more servers, more cost, roughly linear. LLM costs don't follow this pattern. A single prompt can cost $0.001 or $0.50 depending on complexity and length. A viral feature can increase costs 100x overnight.

Without proper FinOps practices, AI costs become one of the fastest-growing — and least visible — line items in engineering budgets.

The Four Pillars of AI Cost Control

1. Attribution: Know Who's Spending What

The first step is tagging every LLM request with a user ID, session ID, and feature name. This enables:

• Identifying which users generate disproportionate costs
• Attributing costs to product features for ROI analysis
• Detecting abuse or runaway automation

await fetch("https://neuralrouting.io/v1/dispatch", {
  body: JSON.stringify({
    messages: [...],
    user_id: "user_12345",       // attribute to user
    session_id: "sess_abc",      // attribute to session
    metadata: { feature: "chat" } // attribute to feature
  })
});