How to Monitor AI Costs: A Simple Guide

By the TechScalpel team · Published November 4, 2026

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You cannot cut what you cannot see. Good monitoring takes an afternoon to set up and usually pays for itself quickly.

Step 1: Log every request

Record the model, input tokens, output tokens and cost for each call. Add a label for the feature and the user or team.

Step 2: Use a dashboard

You can start with your provider's usage page. As you grow, open-source observability tools and AI gateways show cost by feature, model and user in one place.

Step 3: Set budgets and alerts

Set a monthly limit and alerts at 50%, 80% and 100%. Hard caps per key or team stop a runaway job from draining your budget.

Step 4: Review weekly

Look at the three most expensive features. Ask: can a smaller model do this? Can we cache it? Can we shorten the prompt?

Step 5: Watch for loops and retries

Agents and automatic retries can multiply costs fast. A sudden jump in tokens per request is a warning sign.

Track unit costs

Cost per user, per task or per document tells you whether your product can make money, not just how much you spent.

See our reviews of Langfuse, LiteLLM and Portkey.