Performance Analytics

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The Performance Analytics page provides comprehensive insights into LLM performance across models, features, and requests, enabling data-driven decisions for optimization and enhanced user experience.

To access the report, go to Analytics > Gen AI Analytics > Performance Analytics. You can also access this report from the dashboard by selecting a model or feature in its corresponding widget.

Apply filters at the top of the page by module, model, feature, and date range to focus on specific interactions and drill down into targeted performance metrics.

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Field Descriptions

You can sort the data alphabetically or by values in ascending or descending order. Click any record to view detailed model performance metrics, including request trends, token consumption patterns, and latency analysis.

Field	Description
Model	The Large Language Model to which the request was made.
Feature	The platform feature making calls to the LLM models.
Requests	Total number of LLM calls made for that model–feature combination.
Successful Requests	Number of LLM calls that executed without errors.
Failed Requests	Number of LLM calls that encountered errors. Together with successful requests, provides an execution breakdown to monitor system reliability.
Total Tokens	Combined token count for request and response.
Request Tokens	The individual parts of input text (words, punctuation) given to the model to create a response. These tokens form the basis for the model’s understanding.
Response Tokens	The pieces of generated output (words, punctuation) showing the model’s response. These tokens form the structured output of the model.
Avg. Tokens per Request	Average token consumption for each request.
Avg. Tokens per Response	Average token consumption for each response.
Average Response Time	The mean time taken by the model to return a response.
Median Latency	Median processing time, useful for understanding response consistency.