Monitor Azure AI Inference using OpenTelemetry

Shield360 uses OpenTelemetry Auto-Instrumentation to help you monitor LLM applications built using Azure Azure AI Inference models. This includes tracking performance, token usage, costs, and how users interact with the application.

Auto-instrumentation means you don’t have to set up monitoring manually for different LLMs, frameworks, or databases. By simply adding Shield360 in your application, all the necessary monitoring configurations are automatically set up.

The integration is compatible with

• azure-ai-inference Python SDK client >=1.0.0b4
• Shield360 Typescript SDK >= 1.0.0

Get started

Install Shield360

Open your command line or terminal and run:

Python

pip install shield360

Typescript

npm install shield360

Initialize Shield360 in your Application

Python

Zero Code Instrumentation

Perfect for existing applications - no code modifications needed:

Via CLI Arguments

# Configure via CLI arguments
shield360-instrument \
  --service-name my-ai-app \
  --environment production \
  --otlp-endpoint YOUR_OTEL_ENDPOINT \
  python your_app.py

Via Environment Variables

# Configure via environment variables
export OTEL_SERVICE_NAME=my-ai-app
export OTEL_DEPLOYMENT_ENVIRONMENT=production
export OTEL_EXPORTER_OTLP_ENDPOINT=YOUR_OTEL_ENDPOINT

# Run with zero code changes
shield360-instrument python your_app.py

Note

Perfect for: Legacy applications, production systems where code changes need approval, quick testing, or when you want to add observability without touching existing code.

One-Line Instrumentation

Via Function Parameters

import shield360

shield360.init(otlp_endpoint="YOUR_OTEL_ENDPOINT")

Via Environment Variables

Add the following two lines to your application code:

import shield360

shield360.init()

Then, configure the your OTLP endpoint using environment variable:

export OTEL_EXPORTER_OTLP_ENDPOINT=YOUR_OTEL_ENDPOINT

Typescript

One-Line Instrumentation (SDK)

Via Function Parameters

import shield360 from "shield360"

shield360.init({ otlpEndpoint: "YOUR_OTEL_ENDPOINT" })

Via Environment Variables

Add the following two lines to your application code:

import shield360 from "shield360"

shield360.init()

Then, configure the your OTLP endpoint using environment variable:

export OTEL_EXPORTER_OTLP_ENDPOINT=YOUR_OTEL_ENDPOINT

Replace: YOUR_OTEL_ENDPOINT with the URL of your OpenTelemetry backend, such as http://127.0.0.1:4318 if you are using Shield360 and a local OTel Collector.

To send metrics and traces to other Observability tools, refer to the supported destinations.

For more advanced configurations and application use cases, visit the SDK configuration reference or TypeScript SDK reference.

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Quickstart: LLM Observability

Production-ready AI monitoring setup in 2 simple steps with zero code changes

Configuration

Configure the Shield360 SDK according to you requirements.

Destinations

Send telemetry to Datadog, Grafana, New Relic, and other observability stacks