Prometheus

Alerting gets described as a monitoring feature far too often. That framing is convenient, but it hides the real problem.

I keep coming back to llama.cpp for local inference—it gives you control that Ollama and others abstract away, and it just works. Easy to run GGUF models interactively with llama-cli or expose an OpenAI-compatible HTTP API with llama-server.

LLM inference looks like “just another API” — until latency spikes, queues back up, and your GPUs sit at 95% memory with no obvious explanation.

LLM systems fail in ways that traditional API monitoring cannot surface — queues fill silently, GPU memory saturates long before CPU looks busy, and latency blows up at the batching layer rather than the application layer. This guide covers an end-to-end observability strategy for LLM inference and LLM applications: what to measure, how to instrument it with Prometheus, OpenTelemetry, and Grafana, and how to deploy the telemetry pipeline at scale.

Observability is the foundation of reliable production systems.

Without metrics, dashboards, and alerting, Kubernetes clusters drift, AI workloads fail silently, and latency regressions go unnoticed until users complain.

Prometheus has become the de facto standard for monitoring cloud-native applications and infrastructure, offering metrics collection, querying, and integration with visualization tools.

Grafana is the leading open-source platform for monitoring and observability, transforming metrics, logs, and traces into actionable insights through stunning visualizations.