Monitoring AI Applications with Langfuse, Prometheus, and Grafana

Why LLM Observability Is Different

Traditional application monitoring answers questions like "is the service up" and "how fast are responses." LLM applications need those same answers, but also need a second layer entirely: what prompt actually produced this output, how many tokens did it consume, which retrieved documents fed into this answer, and did the response meet quality expectations. A dashboard showing 99.9% uptime and 200ms latency tells you nothing about whether your RAG pipeline is hallucinating or your agent is calling the wrong tools. Both layers are necessary, and they come from different tools.

Langfuse: Tracing at the Application Layer

Langfuse captures the full execution trace of an LLM request — the prompt sent, the model used, token counts, latency per step, and for agent or RAG pipelines, every intermediate step including retrieved documents and tool calls. This turns debugging from guesswork into inspection: when a user reports a wrong answer, you can open the exact trace, see which documents were retrieved and what prompt was constructed, and identify whether the failure was in retrieval, the model, or the prompt itself. Langfuse also supports evaluation scoring, letting teams track quality metrics over time as prompts and models change.

Prometheus and Grafana: Metrics at the Infrastructure Layer

Prometheus and Grafana cover the layer Langfuse doesn't: GPU utilization, memory pressure, request queue depth, and service-level latency and error rates. For GPU-backed inference, the NVIDIA DCGM Exporter feeds per-GPU metrics — utilization, memory used, temperature, power draw — into Prometheus, and Grafana dashboards built on top of those metrics make it possible to spot underutilized GPUs, catch thermal throttling before it affects latency, and track infrastructure cost against actual usage.

Putting Both Layers Together

Neither layer replaces the other. A trace in Langfuse might show a slow response, but only the Prometheus metrics reveal whether that was caused by GPU saturation, a queue backup, or a cold start. In practice, teams running production LLM applications set up alerting on both: Grafana alerts on infrastructure thresholds like GPU memory or request queue depth, while Langfuse's evaluation scores catch quality regressions that infrastructure metrics would never surface. Running both from the start, rather than bolting on observability after an incident, is significantly cheaper than reconstructing what happened after the fact.

Recommended NexNodo Deployment

Observability tooling runs alongside your existing inference stack rather than requiring separate infrastructure. For a production API deployment, the recommended configuration is a Managed Kubernetes GPU Medium:

• 2× H200 GPU
• 30 vCPU
• 512 GB RAM
• 2 TB Storage
• $7.90/hr per node or $5,767/mo

Deploy Faster

The Private AI API Platform Template ships with Langfuse, Prometheus, and Grafana pre-configured alongside vLLM, so observability is in place before your first production request.