Identify AI model overuse with User Insights

When we launched User Insights last month, we wanted to help teams answer a basic question: What are people actually doing with AI? User Insights gives teams a clearer view of their AI usage, showing which users, applications, tasks, and models are driving traffic. It also highlights user and agent anomalies, helping teams identify unexpected or out-of-control spending and usage before they become larger problems.

Our latest update adds something our users have been asking for: context. 

Since launch, we’ve heard from users that model names and request counts only tell part of the story. They show where traffic is going, but reveal little about the work behind it: is that request a code review, a research task, or an agent making several calls to complete a job? The same token count can represent very different kinds of work, and you can’t evaluate with model choice without understanding the task.

User Insights now shows when a model may be more capable than a task requires, which of your users and agents are driving that usage, and how the task, model, cost, and conversation patterns relate. Teams can use these insights to investigate and make targeted changes within their organization. These capabilities are available for free to AI Gateway users.

Consider a team that has routed its internal AI traffic through AI Gateway. After a few weeks, spending is increasing and some requests feel slower than expected, a common challenge as organizations adopt AI at scale.

There could be several explanations. Developers may be using AI for increasingly complex coding work. Agents may be making too many follow-up calls to complete a task. Or a small group of users or agents may be responsible for a disproportionate share of the organization’s usage.

Tokens and request counts alone cannot show which pattern is driving the increase. Teams need to understand what the traffic represents before deciding whether a model, workflow, or routing rule should change.