Monitoring articles

SLOs for AI applications and agents

Define service-level objectives for AI quality, task success, safety, latency, availability, and cost using measurable user-centered indicators.

LLM cost monitoring: Measure cost per successful task

Connect tokens, model calls, retrieval, tools, retries, and infrastructure with quality and task outcomes to control production LLM costs.

What is AI observability?

AI observability connects traces, metrics, evaluations, feedback, and runtime context so teams can understand and improve models, applications, and agents.

Observability for machine learning

ML observability connects logs, metrics, artifacts, infrastructure signals, and model behavior so teams can debug training and serving systems.

How to use the NGINX Prometheus exporter

Connect NGINX metrics to Prometheus with the NGINX Prometheus exporter and configure scraping for basic service monitoring.

Prometheus exporters: tutorial and best practices

Learn how Prometheus exporters expose third-party metrics, how to build a simple exporter, and what practices keep metrics useful.

Prometheus metrics: types, capabilities, and best practices

Understand Prometheus metric types, collection architecture, storage behavior, custom metrics, and Kubernetes monitoring use cases.