Which open-source applications are commonly used for tracking latency and throughput to LLMs?

Prometheus and Grafana are indeed commonly used open-source applications for tracking latency and throughput to large language models (LLMs). Prometheus is a powerful monitoring and alerting toolkit designed for collecting metrics from configured targets at specified intervals. It excels in recording real-time metrics in a time-series database and is particularly useful for systems that require reliable performance metrics, like those involved with LLMs.

Grafana complements Prometheus by providing a visualization layer that helps in creating dashboards and graphs to represent the collected metrics in a user-friendly manner. This combination allows developers and operators to monitor the performance of LLMs effectively, gaining insights into latency and throughput, which are crucial for optimizing performance and ensuring stable operations.

Other options, while valuable in their respective contexts, do not focus primarily on the tracking of latency and throughput specifically for LLMs. TensorFlow and Keras are primarily frameworks for building and training machine learning models; PyTorch, similarly, is also a machine learning framework, while Apache Kafka is a distributed event streaming platform, which is more related to data ingestion rather than direct tracking of performance metrics for LLMs. Scikit-learn and pandas focus on data manipulation and machine learning model training but do not encompass the infrastructure needed for monitoring metrics