FIGR / UI TEMPLATE

AI Usage Cost Attribution

Break down inference spend by feature, workspace, model and token mix.

This analytics template covers ai product operations & evaluation workflows inside an existing product. All people, records and metrics shown are fictional.

AI Usage Cost Attribution

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At a glance

Screen

Analytics

Primary action

Review AI costs

Pattern

analytics workflow

State

Default, Selected, Filtered, Empty, Warning, Success

Design system

Independent product UI reference; no external / 1mg design system

What this screen helps with

Break down inference spend by feature, workspace, model and token mix. The user needs to review ai costs without losing context.

What to look for

Product areas: AI Overview, Evaluations, Runs, Datasets, Policies. Task fields: AI Product Operations record, Assigned owner, Product area, Review status. Example metrics: Active records, Items needing review, Within policy, Upcoming deadlines.

States to consider

Default, Selected, Filtered, Empty, Warning, Success. Also cover restricted access, interrupted saves and missing or stale data when relevant.

Where it fits

AI Overview → AI Usage Cost Attribution → Review AI costs

Patterns in this screen

analytics, filters, status, record details, review actions

UX choices to review

Show reporting periods, denominators, source freshness and why values changed. Distinguish zero from delayed or missing data.

Review review ai costs with a case such as Feature A · $4.2K. Make room for empty, warning, permission-denied and failed-save states.

Try a similar screen in Figr

Adapt AI Usage Cost Attribution to my existing product and design system. Keep our current screens, components and role rules. Explore default, selected, filtered, empty, warning, success and relevant edge cases before generating editable Figma-ready UI.

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