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):
- SigNoz Cloud account with an active ingestion key
- Self-hosted SigNoz instance
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)

📌 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> 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

✅ 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 → Self-Hosted.

Code-based instrumentation gives you fine-grained control over your telemetry configuration. Use this approach when you need to customize resource attributes, sampling strategies, or integrate with existing observability infrastructure.

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

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

Step 2: Import the necessary modules in your Python application

Traces:

from openinference.instrumentation.openai import OpenAIInstrumentor
from opentelemetry import trace
from opentelemetry.sdk.resources import Resource
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter

Logs:

from opentelemetry.sdk._logs import LoggerProvider, LoggingHandler
from opentelemetry.sdk._logs.export import BatchLogRecordProcessor
from opentelemetry.exporter.otlp.proto.http._log_exporter import OTLPLogExporter
from opentelemetry._logs import set_logger_provider
import logging

Metrics:

from opentelemetry.sdk.metrics import MeterProvider
from opentelemetry.exporter.otlp.proto.http.metric_exporter import OTLPMetricExporter
from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader
from opentelemetry import metrics
from opentelemetry.instrumentation.system_metrics import SystemMetricsInstrumentor
from opentelemetry.instrumentation.httpx import HTTPXClientInstrumentor

Step 3: Set up the OpenTelemetry Tracer Provider to send traces directly to SigNoz Cloud

from opentelemetry.sdk.resources import Resource
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry import trace
import os

resource = Resource.create({"service.name": "<service_name>"})
provider = TracerProvider(resource=resource)
span_exporter = OTLPSpanExporter(
    endpoint= os.getenv("OTEL_EXPORTER_TRACES_ENDPOINT"),
    headers={"signoz-ingestion-key": os.getenv("SIGNOZ_INGESTION_KEY")},
)
processor = BatchSpanProcessor(span_exporter)
provider.add_span_processor(processor)
trace.set_tracer_provider(provider)

<service_name> is the name of your service
OTEL_EXPORTER_TRACES_ENDPOINT → SigNoz Cloud trace endpoint with appropriate region:https://ingest.<region>.signoz.cloud:443/v1/traces
SIGNOZ_INGESTION_KEY → Your SigNoz ingestion key

✅ 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 → Self-Hosted.

Step 4: Instrument Azure OpenAI using OpenAInstrumentor and the configured Tracer Provider

from openinference.instrumentation.openai import OpenAIInstrumentor

OpenAIInstrumentor().instrument(tracer_provider=provider)

📌 Important: Place this code at the start of your application logic — before any Azure OpenAI functions are called or used — to ensure telemetry is correctly captured.

Step 5: Setup Logs

import logging
from opentelemetry.sdk.resources import Resource
from opentelemetry._logs import set_logger_provider
from opentelemetry.sdk._logs import LoggerProvider, LoggingHandler
from opentelemetry.sdk._logs.export import BatchLogRecordProcessor
from opentelemetry.exporter.otlp.proto.http._log_exporter import OTLPLogExporter
import os

resource = Resource.create({"service.name": "<service_name>"})
logger_provider = LoggerProvider(resource=resource)
set_logger_provider(logger_provider)

otlp_log_exporter = OTLPLogExporter(
    endpoint= os.getenv("OTEL_EXPORTER_LOGS_ENDPOINT"),
    headers={"signoz-ingestion-key": os.getenv("SIGNOZ_INGESTION_KEY")},
)
logger_provider.add_log_record_processor(
    BatchLogRecordProcessor(otlp_log_exporter)
)
# Attach OTel logging handler to root logger
handler = LoggingHandler(level=logging.INFO, logger_provider=logger_provider)
logging.basicConfig(level=logging.INFO, handlers=[handler])

logger = logging.getLogger(__name__)

<service_name> is the name of your service
OTEL_EXPORTER_LOGS_ENDPOINT → SigNoz Cloud endpoint with appropriate region:https://ingest.<region>.signoz.cloud:443/v1/logs
SIGNOZ_INGESTION_KEY → Your SigNoz ingestion key

✅ 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 → Self-Hosted.

Step 6: Setup Metrics

from opentelemetry.sdk.resources import Resource
from opentelemetry.sdk.metrics import MeterProvider
from opentelemetry.exporter.otlp.proto.http.metric_exporter import OTLPMetricExporter
from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader
from opentelemetry import metrics
from opentelemetry.instrumentation.system_metrics import SystemMetricsInstrumentor
import os

resource = Resource.create({"service.name": "<service-name>"})
metric_exporter = OTLPMetricExporter(
    endpoint= os.getenv("OTEL_EXPORTER_METRICS_ENDPOINT"),
    headers={"signoz-ingestion-key": os.getenv("SIGNOZ_INGESTION_KEY")},
)
reader = PeriodicExportingMetricReader(metric_exporter)
metric_provider = MeterProvider(metric_readers=[reader], resource=resource)
metrics.set_meter_provider(metric_provider)

meter = metrics.get_meter(__name__)

# turn on out-of-the-box metrics
SystemMetricsInstrumentor().instrument()
HTTPXClientInstrumentor().instrument()

<service_name> is the name of your service
OTEL_EXPORTER_METRICS_ENDPOINT → SigNoz Cloud endpoint with appropriate region:https://ingest.<region>.signoz.cloud:443/v1/metrics
SIGNOZ_INGESTION_KEY → Your SigNoz ingestion key

📌 Note: SystemMetricsInstrumentor provides system metrics (CPU, memory, etc.), and HTTPXClientInstrumentor provides outbound HTTP request metrics such as request duration. These are not Azure OpenAI-specific metrics. Azure OpenAI does not expose metrics as part of their SDK. If you want to add custom metrics to your Azure OpenAI application, see Python Custom Metrics.

✅ 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 → Self-Hosted.

Step 7: 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)

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

Step 1: Install the necessary packages in your Node.js project.

npm install \
  @opentelemetry/api \
  @opentelemetry/sdk-node \
  @opentelemetry/sdk-trace-node \
  @opentelemetry/sdk-logs \
  @opentelemetry/sdk-metrics \
  @opentelemetry/exporter-otlp-http \
  @opentelemetry/instrumentation \
  @opentelemetry/instrumentation-http \
  @opentelemetry/host-metrics \
  @opentelemetry/resources \
  @opentelemetry/semantic-conventions \
  @arizeai/openinference-instrumentation-openai \
  openai

Step 2: Import the necessary modules in your JavaScript/Node.js application

Traces:

const { NodeSDK } = require('@opentelemetry/sdk-node')
const { Resource } = require('@opentelemetry/resources')
const { ATTR_SERVICE_NAME } = require('@opentelemetry/semantic-conventions')
const { NodeTracerProvider } = require('@opentelemetry/sdk-trace-node')
const { BatchSpanProcessor } = require('@opentelemetry/sdk-trace-base')
const { OTLPTraceExporter } = require('@opentelemetry/exporter-otlp-http')
const { registerInstrumentations } = require('@opentelemetry/instrumentation')
const { OpenAIInstrumentation } = require('@arizeai/openinference-instrumentation-openai')

Logs:

const { LoggerProvider, BatchLogRecordProcessor } = require('@opentelemetry/sdk-logs')
const { OTLPLogExporter } = require('@opentelemetry/exporter-otlp-http')
const { logs } = require('@opentelemetry/api')

Metrics:

const { MeterProvider, PeriodicExportingMetricReader } = require('@opentelemetry/sdk-metrics')
const { OTLPMetricExporter } = require('@opentelemetry/exporter-otlp-http')
const { HttpInstrumentation } = require('@opentelemetry/instrumentation-http')
const { HostMetrics } = require('@opentelemetry/host-metrics')
const { metrics } = require('@opentelemetry/api')

Step 3: Set up the OpenTelemetry Tracer Provider to send traces directly to SigNoz Cloud

const { NodeTracerProvider } = require('@opentelemetry/sdk-trace-node')
const { BatchSpanProcessor } = require('@opentelemetry/sdk-trace-base')
const { OTLPTraceExporter } = require('@opentelemetry/exporter-otlp-http')
const { Resource } = require('@opentelemetry/resources')
const { ATTR_SERVICE_NAME } = require('@opentelemetry/semantic-conventions')
const { trace } = require('@opentelemetry/api')

const resource = Resource.default({
  attributes: {
    [ATTR_SERVICE_NAME]: '<service_name>',
  },
})

const provider = new NodeTracerProvider({
  resource: resource,
})

const traceExporter = new OTLPTraceExporter({
  url: process.env.OTEL_EXPORTER_TRACES_ENDPOINT,
  headers: {
    'signoz-ingestion-key': process.env.SIGNOZ_INGESTION_KEY,
  },
})

const spanProcessor = new BatchSpanProcessor(traceExporter)
provider.addSpanProcessor(spanProcessor)
provider.register()

<service_name> is the name of your service
OTEL_EXPORTER_TRACES_ENDPOINT → SigNoz Cloud trace endpoint with appropriate region:https://ingest.<region>.signoz.cloud:443/v1/traces
SIGNOZ_INGESTION_KEY → Your SigNoz ingestion key

✅ 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 → Self-Hosted.

Step 4: Instrument Azure OpenAI using OpenAIInstrumentation and the configured Tracer Provider

const { registerInstrumentations } = require('@opentelemetry/instrumentation')
const { OpenAIInstrumentation } = require('@arizeai/openinference-instrumentation-openai')

registerInstrumentations({
  instrumentations: [
    new OpenAIInstrumentation({
      tracerProvider: provider,
    }),
  ],
})

📌 Important: Place this code at the start of your application logic — before any Azure OpenAI functions are called or used — to ensure telemetry is correctly captured.

Step 5: Setup Logs

const { LoggerProvider, BatchLogRecordProcessor } = require('@opentelemetry/sdk-logs')
const { OTLPLogExporter } = require('@opentelemetry/exporter-otlp-http')
const { Resource } = require('@opentelemetry/resources')
const { ATTR_SERVICE_NAME } = require('@opentelemetry/semantic-conventions')
const { logs } = require('@opentelemetry/api')

const logResource = Resource.default({
  attributes: {
    [ATTR_SERVICE_NAME]: '<service_name>',
  },
})

const loggerProvider = new LoggerProvider({
  resource: logResource,
})

const logExporter = new OTLPLogExporter({
  url: process.env.OTEL_EXPORTER_LOGS_ENDPOINT,
  headers: {
    'signoz-ingestion-key': process.env.SIGNOZ_INGESTION_KEY,
  },
})

loggerProvider.addLogRecordProcessor(new BatchLogRecordProcessor(logExporter))

logs.setGlobalLoggerProvider(loggerProvider)

// Create a logger instance
const logger = logs.getLogger('azure-openai-app')

<service_name> is the name of your service
OTEL_EXPORTER_LOGS_ENDPOINT → SigNoz Cloud endpoint with appropriate region:https://ingest.<region>.signoz.cloud:443/v1/logs
SIGNOZ_INGESTION_KEY → Your SigNoz ingestion key

✅ 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 → Self-Hosted.

Step 6: Setup Metrics

const { MeterProvider, PeriodicExportingMetricReader } = require('@opentelemetry/sdk-metrics')
const { OTLPMetricExporter } = require('@opentelemetry/exporter-otlp-http')
const { Resource } = require('@opentelemetry/resources')
const { ATTR_SERVICE_NAME } = require('@opentelemetry/semantic-conventions')
const { metrics } = require('@opentelemetry/api')
const { HttpInstrumentation } = require('@opentelemetry/instrumentation-http')
const { registerInstrumentations } = require('@opentelemetry/instrumentation')

const metricResource = Resource.default({
  attributes: {
    [ATTR_SERVICE_NAME]: '<service_name>',
  },
})

const metricExporter = new OTLPMetricExporter({
  url: process.env.OTEL_EXPORTER_METRICS_ENDPOINT,
  headers: {
    'signoz-ingestion-key': process.env.SIGNOZ_INGESTION_KEY,
  },
})

const metricReader = new PeriodicExportingMetricReader({
  exporter: metricExporter,
  exportIntervalMillis: 10000,
})

const meterProvider = new MeterProvider({
  resource: metricResource,
  readers: [metricReader],
})

metrics.setGlobalMeterProvider(meterProvider)

// Create a meter instance
const meter = metrics.getMeter('azure-openai-app')

// Register HTTP instrumentation for outbound HTTP metrics
registerInstrumentations({
  instrumentations: [new HttpInstrumentation()],
})

// Initialize host metrics for system-level monitoring
const hostMetrics = new HostMetrics({
  meterProvider: meterProvider,
})
hostMetrics.start()

<service_name> is the name of your service
OTEL_EXPORTER_METRICS_ENDPOINT → SigNoz Cloud endpoint with appropriate region:https://ingest.<region>.signoz.cloud:443/v1/metrics
SIGNOZ_INGESTION_KEY → Your SigNoz ingestion key

📌 Note: HostMetrics provides system-level metrics (CPU, memory, disk, network, etc.), and HttpInstrumentation provides outbound HTTP request metrics such as request duration. These are not Azure OpenAI-specific metrics. Azure OpenAI does not expose metrics as part of their SDK. If you want to add custom metrics to your Azure OpenAI application, see JavaScript Custom Metrics.

✅ 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 → Self-Hosted.

Step 7: Run an example

const OpenAI = require('openai')

const client = new OpenAI({
  apiKey: process.env.AZURE_OPENAI_API_KEY,
  baseURL: 'https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/',
})

async function main() {
  const response = await 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,
  })

  console.log(response.choices[0].message.content)
}

main()

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

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 “Related Logs” button in the trace view to see correlated logs:

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 dashboard 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.

Monitoring Azure OpenAi API with SigNoz

Overview

Prerequisites

Monitoring Azure OpenAI

View Traces, Logs, and Metrics in SigNoz

Dashboard

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