As AI models move from research to production, observability becomes a cornerstone of reliable operations. This article explores the full stack of AI system monitoring, starting with token usage tracking to understand cost and usage patterns, then moving to model inference latency to detect bottlenecks. It covers tools and techniques for tracing requests through the entire pipeline, from API gateways to GPU kernels. The piece emphasizes that without proper observability, teams struggle to debug performance issues, optimize resource allocation, and ensure service-level agreements. For MLOps engineers and platform teams, implementing such monitoring is not optional—it is a prerequisite for scaling AI services efficiently. The article also discusses integration with existing observability platforms like Prometheus and Grafana, and how to set up custom metrics for AI-specific workloads. This signal is particularly relevant as organizations increasingly deploy multiple models in production and need to manage costs and performance at scale.
This article highlights the critical importance of observability in AI systems, covering metrics from token usage to model inference latency. It provides a practical guide for monitoring and optimizing AI pipelines, which is essential for production reliability and cost management.