The O&M of mature AI applications requires comprehensive monitoring of model performance and calls, and integrating this data is especially difficult when multiple models coexist.Vercel AI Gateway's built-in logging and analysis tools address this pain point, providing developers with a unified monitoring platform across models.
The system works on the mechanism that all AI requests passing through the gateway are centrally logged, including key metrics such as invocation time, selected model, number of consumed tokens, response latency, and so on. Developers can view a detailed dashboard through the control panel to analyze various performance trends and cost distributions. For example, it is possible to compare the average response times of different models for the same type of request to identify performance bottlenecks; or analyze which API endpoints consume the most budget. The logs also contain complete request and response content for easy debugging and issue tracking. Compared to building a monitoring system on your own, this set of tools not only eliminates infrastructure development and maintenance costs, but also provides a more specialized analysis of AI-specific metrics.
This answer comes from the articleVercel AI Gateway: a gateway to manage and optimize AI application requestsThe
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