Monitoring and Observability for AI Environments

“You only control what you can measure — and with AI, that’s even more critical.”

Why monitoring AI is different

AI environments behave differently from traditional workloads. A model may be running and still deliver incorrect results, high latency, or unexpected costs.

Common scenarios:

• The model looks fine but predictions are degraded.

• The GPU is active but underutilized.

• Inference responds, but with high latency noticeable to users.

• Costs spike suddenly due to the volume of processed tokens.

Conclusion: Observability isn’t optional — it’s a core part of AI reliability.

What to monitor in AI workloads

Layer/Category	Key Metrics	Tools/Sources
Compute (GPU/CPU)	Utilization, memory, temperature, failures	DCGM, nvidia-smi, Azure Monitor
Model (ML/LLM)	Accuracy, inference latency, TPM/RPM	Application Insights, Azure ML, AOAI Logs
Network	Throughput, jitter, slow connections	Azure Monitor for Network
Data	Integrity, freshness, ingestion failures	Data Factory, Synapse, Log Analytics
Cost	GPU usage, token volume, inference time	Cost Management + Log Analytics
Security / Compliance	Secret access, Key Vault logs	Azure Policy, Defender for Cloud

💡 Tip: Monitor both the model behavior and the infrastructure that supports it. Inference without GPU visibility is incomplete diagnosis.

Observability tools in Azure

Tool	Main function
Azure Monitor	Collect and visualize resource metrics and logs
Log Analytics Workspace	Store logs / Advanced KQL queries
Azure Managed Prometheus	Custom metrics, mainly for AKS and GPU
Grafana	Real-time dashboard visualization
Application Insights	Telemetry, response time, tracing
Azure ML Studio	Model and endpoint monitoring
OpenTelemetry Collector	Standardized metrics and export

Practical example: Monitoring GPUs in AKS

Install NVIDIA’s DCGM Exporter:

helm repo add nvidia https://nvidia.github.io/gpu-monitoring-tools
helm install dcgm-exporter nvidia/dcgm-exporter

Integrate with Prometheus or Azure Managed Prometheus:

helm repo add prometheus-community https://prometheus-community.github.io/helm-charts
helm install prom prometheus-community/kube-prometheus-stack

Visualize in Grafana

Add panels with metrics such as:

• DCGM_FI_DEV_GPU_UTIL

• DCGM_FI_DEV_FB_USED

• DCGM_FI_DEV_MEM_COPY_UTIL

💡 These metrics help detect GPU bottlenecks and optimize cluster usage.

Inference latency and performance

Use Application Insights to track:

• duration — average response time

• successRate — success percentage

• dependency calls — external API response time

Add the App Insights or OpenTelemetry SDK directly to the inference code:

from opentelemetry import trace
from azure.monitor.opentelemetry import configure_azure_monitor
configure_azure_monitor()

🔧 Recommendations:

• Track p95/p99 latency per endpoint.

• Create alerts for HTTP 429/503 (throttling and timeouts).

• Correlate model metrics, token usage, and runtime data.

Cost observability

GPUs and tokens are expensive — and fast.

Item	How to monitor
GPU usage per hour	Azure Monitor + Metrics Explorer
Token consumption (TPM/RPM)	Azure OpenAI Metrics/Logs
Cost per project/team	Cost Management with tags (Project=AI, Team=DataScience)
Future cost forecasting	Azure Anomaly Detector or Machine Learning

💡 Create automatic alerts for spending >10% above weekly average.

Predictive analysis and intelligent autoscaling

Use AI to improve your own observability:

• Predict GPU usage peaks based on historical data.

• Detect latency anomalies using Azure Anomaly Detector.

• Trigger intelligent autoscaling (AKS / VMSS) based on inferred load.

Alerts and automated responses

Event	Recommended action
GPU > 90% for 30min	Check data bottlenecks or replicate pods
Latency > 1s	Validate network or model performance
Ingestion failure	Trigger fallback pipeline
Accuracy drop	Retrain or activate previous version

Configure Azure Monitor Action Groups to trigger emails, Logic Apps, or webhooks automatically.

Hands-On: Querying GPU metrics via Kusto (Log Analytics)

Perf
| where ObjectName == "GPU Adapter"
| summarize avg(CounterValue) by bin(TimeGenerated, 5m), CounterName

Combine with Application Insights logs to correlate GPU usage with inference latency.

Best practices for security and observability

• Never log sensitive data (prompts, PII, user responses).

• Enable automatic diagnostics with Azure Policy.

• Centralize logs from all services in a single workspace.

• Keep at least 30 days of retention for audit purposes.

• Use Managed Identity + Key Vault for secrets and authentication.

AI observability checklist

• GPU metrics (DCGM, Prometheus)

• Inference latency monitoring

• Centralized and correlated logs

• Alerts for failures and throttling

• Dashboards with TPM, QPS, tokens, and cost

References

• Monitoring AKS with GPUs

• Azure Monitor Documentation

• OpenTelemetry with Azure

“Good infrastructure is invisible when it works — but poorly monitored AI shows up quickly, either in your monthly bill or in the user experience.”