Monitoring AI Applications: Observability Patterns for LLM Systems
LLM systems have unique characteristics: non-deterministic outputs, variable latency, complex cost models, and quality metrics that require semantic…
7 articles
LLM systems have unique characteristics: non-deterministic outputs, variable latency, complex cost models, and quality metrics that require semantic…
Configure alerts for latency spikes, error rate increases, and token usage anomalies to catch issues before they impact users.
Monitor these essential AI metrics: latency (p50, p95, p99), token consumption per request, error rates by model and endpoint, cache hit rates for…
Log prompts and responses for failed interactions to a secure store for debugging. Implement sampling for successful calls to control storage costs while…
Azure Monitor for Containers (the Container Insights feature) is the native Azure observability solution for AKS that doesn't require running your own…
Container Insights is the Azure Monitor feature that closes the observability gap for AKS clusters—without it, you have Kubernetes metrics available in the…
Operational dashboards in Azure used to mean Power BI, a custom data export, and a refresh schedule nobody could remember. Workbooks killed all of that for…