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Revue d'architecture
Perspectives · 8 min read ·juillet 10, 2026

LLMOps Observability: Metrics That Predict Failure

Beyond latency: retrieval precision, faithfulness, cost per successful query, and escalation rate.

LLMOps Kubernetes
Production platform
Visual figure — edit in Studio under Media → Visual figures.

Most dashboards show uptime. Production AI fails on quality and cost.

Metrics that matter

Metric Why
Retrieval precision@k Wrong context → confident wrong answers
Faithfulness score Hallucination under load
Cost per successful query Token spend without outcomes

Illustration

Instrument at the gateway — not only inside the notebook.

Graphique des connaissances

En rapport

article ↓ Production AI Patterns That Survive Model Churn paper ↓ Production-Grade RAG Blueprint architecture ↓ Kubernetes AI Inference Platform lab ↓ Deploy vLLM on EKS article ↓ LLMOps Observability: Metrics That Predict Failure

Nous aidons les entreprises à utiliser l'IA avec clarté, contrôle et confiance — du premier cas d'usage à une opération IA gouvernée.