Overview
How might we design token usage indicator so people can trust and act on AI output?
When this pattern fits
- Essential for AI-powered applications using API services, developer tools, and platforms where token limits affect functionality and user experience.
When to skip or lighten it
- Products that fully abstract billing and context with no user-facing limits.
- Ultra-short interactions where the meter never moves meaningfully.
- Audiences who find raw token counts confusing (show % of limit instead).
States
State model coming soon
Key UX elements
Key UX elements coming soon
Anti-patterns to avoid
Hitting a context limit with no prior warning.
Showing tokens without explaining what they mean.
Meters that disagree with actual failures.
Cluttering every message with precise token math.
How products use it
| Product | Implementation |
|---|---|
| OpenAI Playground | Token counts on prompts and completions. |
| Anthropic Console | Usage displays for prompt sizing. |
| Cursor | Context and usage cues in the agent UI. |
| GitHub Copilot | Quota and entitlement indicators for plans. |
Implementation
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Generate a production-ready implementation of the "Token Usage Indicator" AI interface design pattern.
Pattern Definition:Frequently asked questions
What should a token usage indicator show?
Percent of context or plan used, and a plain-language tip when near the limit (summarize, start new chat, upgrade).
Tokens vs dollars?
Show tokens or % for context management; dollars for billing. Running meters often combine both during agent runs.
Where should it live?
Near the composer or session header—not only on a billing page after failure.
How does this relate to hard budget ceilings?
The indicator informs. Ceilings enforce a stop when spend or usage hits a cap.