Monitor AI usage and cost together — tokens, requests, and model mix — across every provider in one daily view.

StackSpend ties AI usage signals — requests, token volume, and model mix — directly to the spend they drive across OpenAI, Anthropic, Claude, Cursor, Hugging Face, and Grok. A daily signal surfaces usage and cost in one view instead of separate dashboards, and anomaly detection flags when the token-per-request ratio or model mix shifts, so a usage change is connected to its cost the day it happens.

AI usage monitoring is the practice of tracking requests, token volume, and model mix across AI providers and connecting those usage signals to spend. Because usage and cost are usually tracked in different places, a spike is only linked to its bill after the fact. Usage monitoring closes that gap so longer prompts, more retries, or a premium model default are visible as they change cost, not weeks later.

StackSpend breaks AI usage down by provider and model and exposes the token/request ratio and model mix, then attributes the resulting cost by provider, model, project, and key with tagging to team, product, or feature. That shows exactly which model or workload is driving token volume, so a shift toward a premium model or a background agent multiplying requests is identifiable rather than hidden in a single total.

Yes. StackSpend turns a rising usage trend into a month-end number with pace-to-forecast, so climbing tokens per request or growing request volume become a projected overrun before it lands. Anomaly detection tuned to bursty AI bills flags the usage shift same-day and names the likely driver, giving you time to act while the trend is still correctable.