Monitoring Azure AI APIs is critical for performance, usage tracking, quota management, and troubleshooting. Azure provides multiple built-in and extensible options to monitor its AI services (like Azure OpenAI, Cognitive Services, and Azure Machine Learning). Here's a breakdown of the available monitoring options:

Azure Monitor provides a centralized platform for collecting, analyzing, and acting on telemetry from Azure resources.

• Metrics: Track request volume, latency, and error rates.
• Logs (via Log Analytics): Ingest and query detailed activity and diagnostics logs.
• Alerts: Set up rules to get notified on API failures, high latency, quota breaches, etc.
• Dashboards: Build visual dashboards to monitor trends.

Each AI service exposes its own set of metrics in Azure Monitor:

You can find these under:

Azure Portal → Monitor → Metrics → Select your AI resource

You can configure Diagnostic Settings on each Azure AI resource to send logs and metrics to:

• Log Analytics Workspace
• Event Hubs
• Azure Storage

Logs may include:

• Request logs (time, endpoint, status)
• Quota usage
• Custom logs depending on the service

Enable via:

Resource → Monitoring → Diagnostic settings

If you're calling Azure AI APIs from your own application, you can use Application Insights to:

• Track dependency calls to Azure AI APIs
• Monitor end-to-end latency and failures
• View distributed traces and performance bottlenecks

Integrates well with web apps, functions, and APIs

For services like Azure OpenAI and Cognitive Services:

• Azure Quotas API: Track how close you are to usage limits
• Azure Portal → Usage + Quotas under the AI service

You can set up alerts when usage approaches or exceeds thresholds.

If you're deploying models via Azure ML:

• Azure ML Studio has its own monitoring for:
  • Endpoint request count
  • Response times
  • Status codes
  • Resource utilization (CPU/Memory)

Studio → Endpoints → Monitoring tab

You can build wrappers or proxies around API calls to:

• Log request/response time
• Capture payloads for analysis
• Push logs to Azure Log Analytics or external tools (e.g., Splunk)

Use:

• Microsoft Defender for Cloud: To monitor security posture
• Azure Policy: To enforce tagging, logging, and retention policies
• Sentinel (SIEM): For advanced threat detection and analytics

• Enable Diagnostic Logs and send to Log Analytics
• Configure Alerts for latency, error rate, and quota limits
• Use Workbooks for visual monitoring dashboards
• Use App Insights for client-side monitoring if you build apps that call the APIs