AI cost tracking for production LLMs: tools, patterns, and what to measure

Why generic FinOps misses LLM spend

Cloud cost dashboards show service totals. LLM spend is per-request, per-model, and per-feature — often buried inside application logs.

What to measure first

Start with four metrics:

1. Cost per workflow — not per API key
2. Tokens in vs. tokens out by model
3. Cache hit rate on repeated prompts
4. Escalation rate to expensive models

Tooling that works in production

We implement observability with tools teams already adopt: Langfuse, Helicone, LiteLLM gateways, or Datadog LLM monitoring. The goal is attribution finance can read.

Quick wins before a full platform

• Route simple tasks to smaller models
• Cache embeddings for stable document sets
• Batch non-interactive workloads

Takeaway

Make AI spend legible before you scale usage — not after the quarterly review.

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