What is LLM monitoring? (Quality, cost, latency, and drift in production)

Large Language Model (LLM) monitoring is crucial for ensuring the optimal performance of AI applications in production environments. Unlike traditional application monitoring, which focuses on system health metrics like CPU usage and memory, LLM monitoring encompasses both operational metrics—such as latency, error types, and token costs—and quality metrics that assess the accuracy, relevance, and safety of model outputs. This approach fills the gap left by traditional monitoring tools that may report technical success while overlooking the quality of content generated by LLMs, which can be non-deterministic and produce hallucinated or harmful content. LLM monitoring involves several layers, including tracking user prompts and responses, measuring latency, and attributing costs, while also assessing safety and compliance to prevent unsafe outputs. Advanced solutions like Braintrust offer comprehensive monitoring by integrating tracing, quality evaluation, and cost analytics, helping teams detect and address issues before they impact users. By implementing a layered monitoring approach, organizations can transition AI development into a predictable engineering practice, ensuring reliable and safe AI software deployment in real-world scenarios.