How do you observe LLM systems in production?

Large language models (LLMs) are increasingly deployed across various applications, but their performance in production can be unpredictable and costly, necessitating a shift from traditional monitoring to LLM-specific observability. Traditional monitoring may not capture LLM failures, such as generating incorrect outputs or incurring high costs, as these systems can technically "succeed" while failing their purpose. LLM observability requires detailed tracing of each request, revealing issues like slow database lookups or malformed prompts, and tracking performance metrics such as latency, throughput, and error rates, to ensure user satisfaction and cost efficiency. Cost observability is crucial to prevent unexpected expenses by monitoring token usage and setting budget alerts. Quality monitoring is essential to detect hallucinations, ensure relevance, and maintain safety, while user feedback provides valuable insights for continuous improvement. Tools such as PromptLayer and others offer integrated solutions for tracing, cost analytics, and prompt management, helping teams reduce costs and improve model performance. Implementing observability from the outset and aligning KPIs with business goals are vital for maintaining trust in LLMs and addressing issues proactively.