Token-based pricing has become the standard commercial model for large language model services. A token is a unit of text — roughly a word or a fragment of a word — and input and output tokens are typically metered separately.
For enterprises, the operational challenge is not the unit price but the aggregate behaviour of many concurrent callers. Without quotas, a single misconfigured service can consume an entire monthly allowance in hours. Practical controls include per-application rate limits, hard monthly caps, alerting at defined usage thresholds, and separation of production from experimental traffic.
Cost predictability improves further when workloads are classified. Batch and asynchronous jobs can be scheduled into lower-cost windows, while latency-sensitive user-facing traffic is reserved on dedicated capacity. Caching repeated prompts and trimming unnecessary context are the two most effective engineering levers.
Visibility is what makes all of this manageable. When consumption can be seen by application, limits can be adjusted by the teams that own the workload rather than escalating to a central owner every time.
