SigNoz Cloud - This page is relevant for SigNoz Cloud editions.
Self-Host - This page is relevant for self-hosted SigNoz editions.

Monitoring Azure OpenAi API with SigNoz

Overview

This guide walks you through setting up monitoring and observability for Azure OpenAi API using OpenTelemetry and exporting logs, traces, and metrics to SigNoz. With this integration, you can observe model performance, capture request/response details, and track system-level metrics in SigNoz, giving you real-time visibility into latency, error rates, and usage trends for your Azure OpenAi applications.

Many developers choose Azure OpenAI over regular OpenAI for enterprise-grade features including enhanced security and compliance certifications, private network integration with Azure Virtual Networks, regional data residency options, integration with Azure Active Directory for identity management, dedicated capacity with provisioned throughput, and seamless integration with other Azure services. These capabilities make Azure OpenAI particularly valuable for organizations with strict regulatory requirements or those already invested in the Azure ecosystem.

Instrumenting Azure OpenAi in your LLM applications with telemetry ensures full observability across your AI workflows, making it easier to debug issues, optimize performance, and understand user interactions. By leveraging SigNoz, you can analyze correlated traces, logs, and metrics in unified dashboards, configure alerts, and gain actionable insights to continuously improve reliability, responsiveness, and user experience.

Prerequisites

  • SigNoz setup (choose one):
  • Internet access to send telemetry data to SigNoz Cloud
  • An Microsoft Azure account with an OpenAI resource deployed and working API Key
  • For Python: pip installed for managing Python packages and (optional but recommended) a Python virtual environment to isolate dependencies
  • For JavaScript: Node.js (version 14 or higher) and npm installed for managing Node.js packages

Monitoring Azure OpenAI

The Azure OpenAI API uses an API format compatible with OpenAI. By modifying the configuration, you can use the OpenAI SDK or softwares compatible with the OpenAI API to access the Azure OpenAI API. Hence, a similar method to monitor OpenAI APIs can be used for monitoring Azure OpenAI APIs as well. To read more about this, you can read the Azure OpenAI API Docs

No-code auto-instrumentation is recommended for quick setup with minimal code changes. It's ideal when you want to get observability up and running without modifying your application code and are leveraging standard instrumentor libraries.

Step 1: Install the necessary packages in your Python environment.

pip install \
  opentelemetry-distro \
  opentelemetry-exporter-otlp \
  opentelemetry-instrumentation-httpx \
  opentelemetry-instrumentation-system-metrics \
  openai \
  openinference-instrumentation-openai

Step 2: Add Automatic Instrumentation

opentelemetry-bootstrap --action=install

Step 3: Configure logging level

To ensure logs are properly captured and exported, configure the root logger to emit logs at the INFO level or higher:

import logging
logging.getLogger().setLevel(logging.INFO)

This sets the minimum log level for the root logger to INFO, which ensures that logger.info() calls and higher severity logs (WARNING, ERROR, CRITICAL) are captured by the OpenTelemetry logging auto-instrumentation and sent to SigNoz.

Step 4: Run an example

import openai
import os

client = OpenAI(api_key=os.getenv("AZURE_OPENAI_API_KEY"), base_url="https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/")

response = client.chat.completions.create(
    model="<your-model-deployment-name>",
    messages=[
        {"role": "system", "content": "You are a helpful assistant"},
        {"role": "user", "content": "What is SigNoz?"},
    ],
    stream=False
)

print(response.choices[0].message.content)

πŸ“Œ Note: Before running this code, ensure that you have set the environment variable AZURE_OPENAI_API_KEY with your working API key.

Step 5: Run your application with auto-instrumentation

OTEL_RESOURCE_ATTRIBUTES="service.name=<service_name>" \
OTEL_EXPORTER_OTLP_ENDPOINT="https://ingest.<region>.signoz.cloud:443" \
OTEL_EXPORTER_OTLP_HEADERS="signoz-ingestion-key=<your_ingestion_key>" \
OTEL_EXPORTER_OTLP_PROTOCOL=grpc \
OTEL_TRACES_EXPORTER=otlp \
OTEL_METRICS_EXPORTER=otlp \
OTEL_LOGS_EXPORTER=otlp \
OTEL_PYTHON_LOG_CORRELATION=true \
OTEL_PYTHON_LOGGING_AUTO_INSTRUMENTATION_ENABLED=true \
opentelemetry-instrument <your_run_command>
  • <service_name>Β is the name of your service
  • Set the <region> to match your SigNoz Cloud region
  • Replace <your_ingestion_key> with your SigNoz ingestion key
  • Replace <your_run_command> with the actual command you would use to run your application. For example: python main.py
βœ… Info

Using self-hosted SigNoz? Most steps are identical. To adapt this guide, update the endpoint and remove the ingestion key header as shown in Cloud β†’ Self-Hosted.

View Traces, Logs, and Metrics in SigNoz

Your Azure OpenAI commands should now automatically emit traces, logs, and metrics.

You should be able to view traces in Signoz Cloud under the traces tab:

Azure OpenAI Trace View
Azure OpenAI API Trace View

When you click on a trace in SigNoz, you'll see a detailed view of the trace, including all associated spans, along with their events and attributes.

Azure OpenAI Detailed Trace View
Azure OpenAI API Detailed Trace View

You should be able to view logs in Signoz Cloud under the logs tab. You can also view logs by clicking on the β€œRelated Logs” button in the trace view to see correlated logs:

Related logs
Related logs button
Azure OpenAI Logs View
Azure OpenAI API Logs View

When you click on any of these logs in SigNoz, you'll see a detailed view of the log, including attributes:

Azure OpenAI Detailed Log View
Azure OpenAI API Detailed Logs View

You should be able to see Azure OpenAI related metrics in Signoz Cloud under the metrics tab:

Azure OpenAI Metrics View
Azure OpenAI API Metrics View

When you click on any of these metrics in SigNoz, you'll see a detailed view of the metric, including attributes:

Azure OpenAI Detailed Metrics View
Azure OpenAI API Detailed Metrics View

Dashboard

You can also check out our custom Azure OpenAI API dashboardΒ here which provides specialized visualizations for monitoring your Azure OpenAI API usage in applications. The dashboard includes pre-built charts specifically tailored for LLM usage, along with import instructions to get started quickly.

Azure OpenAI Dashboard
Azure OpenAI API Dashboard Template

Last updated: September 15, 2025

Edit on GitHub

Was this page helpful?