Why Observability Matters (More!) with AI Applications

AI-powered applications require specialized monitoring approaches because they're fundamentally different from traditional microservices—featuring unpredictable performance patterns, expensive GPU costs averaging $5 hourly, and complex multi-stage processes like retrieval and generation phases. Red Hat demonstrates setting up an open-source observability stack using Prometheus, Grafana, OpenTelemetry, and Tempo with vLLM and Llama Stack to track performance, cost, and quality metrics essential for production AI deployments.