Developers are increasingly using Tinybird to track LLM usage, costs, and performance in AI applications. A new app template called the LLM Performance Tracker allows users to generate real-time data visualizations. The core components include a Tinybird datasource, a Tinybird pipe, a React component, and an AI API route. The backend processes user input to generate chart parameters, while the frontend visualizes the data. This approach emphasizes the importance of performant analytics backends and cautious LLM usage for secure and scalable data visualization.
Table of contents
The componentsStoring and processing LLM calls with TinybirdDefining an API route to generate structured parameters from user inputGathering user input and displaying the chart in the UIThe result: A dynamic chart that matches user intentDiscussion: Why not use LLMs for everything?Get startedSort: