vibestats/Guides/How to measure AI token costs

Guide

How to measure AI token costs

A practical guide to measuring token volume and estimated cost in vibestats.

Intent
Guide
Focus
How to measure AI token costs is a practical workflow page for turning local AI coding usage into something readable and repeatable.

Highlights

  • Totals are the cleanest starting point
  • Tokens and cost are related but not identical
  • Model views help explain cost shifts

Relevant commands

npx vibestats --totalnpx vibestats --monthlynpx vibestats --model

Step 1

Start with totals

Use total or monthly views first so token and cost signals are easier to read than in a noisy daily table.

Step 2

Separate volume from price

Read token categories and cost estimates together so you do not confuse high usage with expensive usage automatically.

Step 3

Drill down by model or source

Use model and source-specific views when the budget question is tied to a specific tool or family.

Why this guide exists

Most AI coding reporting problems are not about one missing command. They are about choosing the right surface: daily usage, wrapped summaries, activity heatmaps, cost views, or shareable aggregate pages.

What to expect

This guide stays focused on vibestats workflows and the pages already documented in the public command reference, so you can move from local data to a readable result quickly.

FAQ

What is the fastest way to approach how to measure ai token costs?

Start with the relevant command, verify the output locally, then decide whether you need a share page, a wrapped summary, or a heatmap for communication.

Do I need to upload raw conversations for these guides?

No. vibestats works from local usage artifacts and only turns aggregate results into hosted pages when you explicitly publish them.

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