"How do I monitor OpenAI usage?" usually means one of two things: how much am I using and what is it costing me. The native OpenAI usage dashboard answers the first slowly and the second separately — which is why usage spikes get noticed at the invoice.
Here's a practical approach.
1. Watch usage and cost together
The single most useful change is to stop looking at usage and billing on different screens. Tie requests, input/output tokens, and model mix to the dollars they generate. A usage change only matters because of its cost — so view them in the same place.
2. Track tokens per request, not just totals
Total usage can look flat while cost climbs, because tokens-per-request grew. That's the early-warning signal for OpenAI: a prompt change, a longer retrieved context, or more verbose output raises tokens-per-request before traffic changes. Watch it against a baseline.
3. Break it down by model and project
gpt-4o vs gpt-4o-mini is a large cost difference at the same token volume. Monitor the share of usage by model so you catch a routing change where a premium model becomes the default. Attribute usage to projects so you know which workload is responsible.
4. Get a signal, not a habit
Logging into a dashboard is a habit that fails exactly when you're busy. The reliable pattern is a daily signal plus anomaly alerts that reach you when usage shifts.
The fast path
StackSpend's OpenAI usage monitoring connects with your Organization ID and API key (read-only) and does all four: usage tied to cost, tokens-per-request tracking, per-model breakdown, and a daily signal with anomaly alerts. For the cost-specific view, see OpenAI cost monitoring and OpenAI API cost monitoring; to alert on it, OpenAI spend alerts.