{"slug": "azure-openai-monitoring-observability-with-opentelemetry", "title": "Azure OpenAI Monitoring & Observability with OpenTelemetry", "summary": "Microsoft has released a new monitoring and observability integration for Azure OpenAI that uses OpenTelemetry to export logs, traces, and metrics to SigNoz, enabling developers to track model performance, latency, error rates, and usage trends in real time. The integration, which supports both Python and JavaScript environments, provides enterprise-grade visibility into AI workflows for organizations using Azure OpenAI's security, compliance, and private network features. Developers can implement the monitoring with no-code auto-instrumentation and unified dashboards to debug issues, optimize performance, and configure alerts across their LLM applications.", "body_md": "Overview\n\nThis guide walks you through setting up monitoring and observability for Azure OpenAi API using [OpenTelemetry](https://opentelemetry.io/) 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.\n\nMany 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.\n\nInstrumenting 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.\n\nPrerequisites\n\n- SigNoz setup (choose one):\n[SigNoz Cloud account](https://signoz.io/teams/)with an active ingestion key- Self-hosted SigNoz instance\n\n- Internet access to send telemetry data to SigNoz Cloud\n- An\n[Microsoft Azure](https://azure.microsoft.com/en-us/pricing/purchase-options/azure-account/)account with an OpenAI resource deployed and working API Key - For Python:\n`pip`\n\ninstalled for managing Python packages and*(optional but recommended)*a Python virtual environment to isolate dependencies - For JavaScript: Node.js (version 14 or higher) and\n`npm`\n\ninstalled for managing Node.js packages\n\nMonitoring Azure OpenAI\n\nThe 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](https://learn.microsoft.com/en-us/azure/ai-foundry/openai/how-to/responses?tabs=python-key)\n\nNo-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.\n\n**Step 1:** Install the necessary packages in your Python environment.\n\n```\npip install \\\n  opentelemetry-distro \\\n  opentelemetry-exporter-otlp \\\n  opentelemetry-instrumentation-httpx \\\n  opentelemetry-instrumentation-system-metrics \\\n  openai \\\n  openinference-instrumentation-openai\n```\n\n**Step 2:** Add Automatic Instrumentation\n\n```\nopentelemetry-bootstrap --action=install\n```\n\n**Step 3:** Configure logging level\n\nTo ensure logs are properly captured and exported, configure the root logger to emit logs at the INFO level or higher:\n\n``` python\nimport logging\nlogging.getLogger().setLevel(logging.INFO)\n```\n\nThis sets the minimum log level for the root logger to INFO, which ensures that `logger.info()`\n\ncalls and higher severity logs (WARNING, ERROR, CRITICAL) are captured by the OpenTelemetry logging auto-instrumentation and sent to SigNoz.\n\n**Step 4:** Run an example\n\n``` python\nimport openai\nimport os\n\nclient = OpenAI(api_key=os.getenv(\"AZURE_OPENAI_API_KEY\"), base_url=\"https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/\")\n\nresponse = client.chat.completions.create(\n    model=\"<your-model-deployment-name>\",\n    messages=[\n        {\"role\": \"system\", \"content\": \"You are a helpful assistant\"},\n        {\"role\": \"user\", \"content\": \"What is SigNoz?\"},\n    ],\n    stream=False\n)\n\nprint(response.choices[0].message.content)\n```\n\n📌 Note: Before running this code, ensure that you have set the environment variable\n\n`AZURE_OPENAI_API_KEY`\n\nwith your working API key.\n\n**Step 5:** Run your application with auto-instrumentation\n\n```\nOTEL_RESOURCE_ATTRIBUTES=\"service.name=<service_name>\" \\\nOTEL_EXPORTER_OTLP_ENDPOINT=\"https://ingest.<region>.signoz.cloud:443\" \\\nOTEL_EXPORTER_OTLP_HEADERS=\"signoz-ingestion-key=<your-ingestion-key>\" \\\nOTEL_EXPORTER_OTLP_PROTOCOL=grpc \\\nOTEL_TRACES_EXPORTER=otlp \\\nOTEL_METRICS_EXPORTER=otlp \\\nOTEL_LOGS_EXPORTER=otlp \\\nOTEL_PYTHON_LOG_CORRELATION=true \\\nOTEL_PYTHON_LOGGING_AUTO_INSTRUMENTATION_ENABLED=true \\\nopentelemetry-instrument <your_run_command>\n```\n\nis the name of your service`<service_name>`\n\n`<region>`\n\n: Your[SigNoz Cloud region](https://signoz.io/docs/ingestion/signoz-cloud/overview/#endpoint)`<your-ingestion-key>`\n\n: Your SigNoz[ingestion key](https://signoz.io/docs/ingestion/signoz-cloud/keys/)- Replace\n`<your_run_command>`\n\nwith the actual command you would use to run your application. For example:`python main.py`\n\nUsing 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](https://signoz.io/docs/ingestion/cloud-vs-self-hosted/#cloud-to-self-hosted).\n\nCode-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.\n\n**Step 1:** Install the necessary packages in your Python environment.\n\n```\npip install \\\n  opentelemetry-api \\\n  opentelemetry-sdk \\\n  opentelemetry-exporter-otlp \\\n  opentelemetry-instrumentation-httpx \\\n  opentelemetry-instrumentation-system-metrics \\\n  openai \\\n  openinference-instrumentation-openai\n```\n\n**Step 2:** Import the necessary modules in your Python application\n\n**Traces:**\n\n``` python\nfrom openinference.instrumentation.openai import OpenAIInstrumentor\nfrom opentelemetry import trace\nfrom opentelemetry.sdk.resources import Resource\nfrom opentelemetry.sdk.trace import TracerProvider\nfrom opentelemetry.sdk.trace.export import BatchSpanProcessor\nfrom opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter\n```\n\n**Logs:**\n\n``` python\nfrom opentelemetry.sdk._logs import LoggerProvider, LoggingHandler\nfrom opentelemetry.sdk._logs.export import BatchLogRecordProcessor\nfrom opentelemetry.exporter.otlp.proto.http._log_exporter import OTLPLogExporter\nfrom opentelemetry._logs import set_logger_provider\nimport logging\n```\n\n**Metrics:**\n\n``` python\nfrom opentelemetry.sdk.metrics import MeterProvider\nfrom opentelemetry.exporter.otlp.proto.http.metric_exporter import OTLPMetricExporter\nfrom opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader\nfrom opentelemetry import metrics\nfrom opentelemetry.instrumentation.system_metrics import SystemMetricsInstrumentor\nfrom opentelemetry.instrumentation.httpx import HTTPXClientInstrumentor\n```\n\n**Step 3:** Set up the OpenTelemetry Tracer Provider to send traces directly to SigNoz Cloud\n\n``` python\nfrom opentelemetry.sdk.resources import Resource\nfrom opentelemetry.sdk.trace import TracerProvider\nfrom opentelemetry.sdk.trace.export import BatchSpanProcessor\nfrom opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter\nfrom opentelemetry import trace\nimport os\n\nresource = Resource.create({\"service.name\": \"<service_name>\"})\nprovider = TracerProvider(resource=resource)\nspan_exporter = OTLPSpanExporter(\n    endpoint= os.getenv(\"OTEL_EXPORTER_TRACES_ENDPOINT\"),\n    headers={\"signoz-ingestion-key\": os.getenv(\"SIGNOZ_INGESTION_KEY\")},\n)\nprocessor = BatchSpanProcessor(span_exporter)\nprovider.add_span_processor(processor)\ntrace.set_tracer_provider(provider)\n```\n\nis the name of your service`<service_name>`\n\n→ SigNoz Cloud trace endpoint with appropriate`OTEL_EXPORTER_TRACES_ENDPOINT`\n\n[region](https://signoz.io/docs/ingestion/signoz-cloud/overview/#endpoint):`https://ingest.<region>.signoz.cloud:443/v1/traces`\n\n→ Your SigNoz`SIGNOZ_INGESTION_KEY`\n\n[ingestion key](https://signoz.io/docs/ingestion/signoz-cloud/keys/)\n\nUsing 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](https://signoz.io/docs/ingestion/cloud-vs-self-hosted/#cloud-to-self-hosted).\n\n**Step 4:** Instrument Azure OpenAI using `OpenAInstrumentor`\n\nand the configured Tracer Provider\n\n``` python\nfrom openinference.instrumentation.openai import OpenAIInstrumentor\n\nOpenAIInstrumentor().instrument(tracer_provider=provider)\n```\n\n📌 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.\n\n**Step 5**: Setup Logs\n\n``` python\nimport logging\nfrom opentelemetry.sdk.resources import Resource\nfrom opentelemetry._logs import set_logger_provider\nfrom opentelemetry.sdk._logs import LoggerProvider, LoggingHandler\nfrom opentelemetry.sdk._logs.export import BatchLogRecordProcessor\nfrom opentelemetry.exporter.otlp.proto.http._log_exporter import OTLPLogExporter\nimport os\n\nresource = Resource.create({\"service.name\": \"<service_name>\"})\nlogger_provider = LoggerProvider(resource=resource)\nset_logger_provider(logger_provider)\n\notlp_log_exporter = OTLPLogExporter(\n    endpoint= os.getenv(\"OTEL_EXPORTER_LOGS_ENDPOINT\"),\n    headers={\"signoz-ingestion-key\": os.getenv(\"SIGNOZ_INGESTION_KEY\")},\n)\nlogger_provider.add_log_record_processor(\n    BatchLogRecordProcessor(otlp_log_exporter)\n)\n# Attach OTel logging handler to root logger\nhandler = LoggingHandler(level=logging.INFO, logger_provider=logger_provider)\nlogging.basicConfig(level=logging.INFO, handlers=[handler])\n\nlogger = logging.getLogger(__name__)\n```\n\nis the name of your service`<service_name>`\n\n→ SigNoz Cloud endpoint with appropriate`OTEL_EXPORTER_LOGS_ENDPOINT`\n\n[region](https://signoz.io/docs/ingestion/signoz-cloud/overview/#endpoint):`https://ingest.<region>.signoz.cloud:443/v1/logs`\n\n→ Your SigNoz`SIGNOZ_INGESTION_KEY`\n\n[ingestion key](https://signoz.io/docs/ingestion/signoz-cloud/keys/)\n\nUsing 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](https://signoz.io/docs/ingestion/cloud-vs-self-hosted/#cloud-to-self-hosted).\n\n**Step 6**: Setup Metrics\n\n``` python\nfrom opentelemetry.sdk.resources import Resource\nfrom opentelemetry.sdk.metrics import MeterProvider\nfrom opentelemetry.exporter.otlp.proto.http.metric_exporter import OTLPMetricExporter\nfrom opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader\nfrom opentelemetry import metrics\nfrom opentelemetry.instrumentation.system_metrics import SystemMetricsInstrumentor\nimport os\n\nresource = Resource.create({\"service.name\": \"<service-name>\"})\nmetric_exporter = OTLPMetricExporter(\n    endpoint= os.getenv(\"OTEL_EXPORTER_METRICS_ENDPOINT\"),\n    headers={\"signoz-ingestion-key\": os.getenv(\"SIGNOZ_INGESTION_KEY\")},\n)\nreader = PeriodicExportingMetricReader(metric_exporter)\nmetric_provider = MeterProvider(metric_readers=[reader], resource=resource)\nmetrics.set_meter_provider(metric_provider)\n\nmeter = metrics.get_meter(__name__)\n\n# turn on out-of-the-box metrics\nSystemMetricsInstrumentor().instrument()\nHTTPXClientInstrumentor().instrument()\n```\n\nis the name of your service`<service_name>`\n\n→ SigNoz Cloud endpoint with appropriate`OTEL_EXPORTER_METRICS_ENDPOINT`\n\n[region](https://signoz.io/docs/ingestion/signoz-cloud/overview/#endpoint):`https://ingest.<region>.signoz.cloud:443/v1/metrics`\n\n→ Your SigNoz`SIGNOZ_INGESTION_KEY`\n\n[ingestion key](https://signoz.io/docs/ingestion/signoz-cloud/keys/)\n\n📌 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\n\n[Python Custom Metrics].\n\nUsing 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](https://signoz.io/docs/ingestion/cloud-vs-self-hosted/#cloud-to-self-hosted).\n\n**Step 7:** Run an example\n\n``` python\nimport openai\nimport os\n\nclient = OpenAI(api_key=os.getenv(\"AZURE_OPENAI_API_KEY\"), base_url=\"https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/\")\n\nresponse = client.chat.completions.create(\n    model=\"<your-model-deployment-name>\",\n    messages=[\n        {\"role\": \"system\", \"content\": \"You are a helpful assistant\"},\n        {\"role\": \"user\", \"content\": \"What is SigNoz?\"},\n    ],\n    stream=False\n)\n\nprint(response.choices[0].message.content)\n```\n\n📌 Note: Before running this code, ensure that you have set the environment variable\n\n`AZURE_OPENAI_API_KEY`\n\nwith your working API key.\n\n**Step 1:** Install the necessary packages in your Node.js project.\n\n```\nnpm install \\\n  @opentelemetry/api \\\n  @opentelemetry/sdk-node \\\n  @opentelemetry/sdk-trace-node \\\n  @opentelemetry/sdk-logs \\\n  @opentelemetry/sdk-metrics \\\n  @opentelemetry/exporter-otlp-http \\\n  @opentelemetry/instrumentation \\\n  @opentelemetry/instrumentation-http \\\n  @opentelemetry/host-metrics \\\n  @opentelemetry/resources \\\n  @opentelemetry/semantic-conventions \\\n  @arizeai/openinference-instrumentation-openai \\\n  openai\n```\n\n**Step 2:** Import the necessary modules in your JavaScript/Node.js application\n\n**Traces:**\n\n``` js\nconst { NodeSDK } = require('@opentelemetry/sdk-node')\nconst { Resource } = require('@opentelemetry/resources')\nconst { ATTR_SERVICE_NAME } = require('@opentelemetry/semantic-conventions')\nconst { NodeTracerProvider } = require('@opentelemetry/sdk-trace-node')\nconst { BatchSpanProcessor } = require('@opentelemetry/sdk-trace-base')\nconst { OTLPTraceExporter } = require('@opentelemetry/exporter-otlp-http')\nconst { registerInstrumentations } = require('@opentelemetry/instrumentation')\nconst { OpenAIInstrumentation } = require('@arizeai/openinference-instrumentation-openai')\n```\n\n**Logs:**\n\n``` js\nconst { LoggerProvider, BatchLogRecordProcessor } = require('@opentelemetry/sdk-logs')\nconst { OTLPLogExporter } = require('@opentelemetry/exporter-otlp-http')\nconst { logs } = require('@opentelemetry/api')\n```\n\n**Metrics:**\n\n``` js\nconst { MeterProvider, PeriodicExportingMetricReader } = require('@opentelemetry/sdk-metrics')\nconst { OTLPMetricExporter } = require('@opentelemetry/exporter-otlp-http')\nconst { HttpInstrumentation } = require('@opentelemetry/instrumentation-http')\nconst { HostMetrics } = require('@opentelemetry/host-metrics')\nconst { metrics } = require('@opentelemetry/api')\n```\n\n**Step 3:** Set up the OpenTelemetry Tracer Provider to send traces directly to SigNoz Cloud\n\n``` js\nconst { NodeTracerProvider } = require('@opentelemetry/sdk-trace-node')\nconst { BatchSpanProcessor } = require('@opentelemetry/sdk-trace-base')\nconst { OTLPTraceExporter } = require('@opentelemetry/exporter-otlp-http')\nconst { Resource } = require('@opentelemetry/resources')\nconst { ATTR_SERVICE_NAME } = require('@opentelemetry/semantic-conventions')\nconst { trace } = require('@opentelemetry/api')\n\nconst resource = Resource.default({\n  attributes: {\n    [ATTR_SERVICE_NAME]: '<service_name>',\n  },\n})\n\nconst provider = new NodeTracerProvider({\n  resource: resource,\n})\n\nconst traceExporter = new OTLPTraceExporter({\n  url: process.env.OTEL_EXPORTER_TRACES_ENDPOINT,\n  headers: {\n    'signoz-ingestion-key': process.env.SIGNOZ_INGESTION_KEY,\n  },\n})\n\nconst spanProcessor = new BatchSpanProcessor(traceExporter)\nprovider.addSpanProcessor(spanProcessor)\nprovider.register()\n```\n\nis the name of your service`<service_name>`\n\n→ SigNoz Cloud trace endpoint with appropriate`OTEL_EXPORTER_TRACES_ENDPOINT`\n\n[region](https://signoz.io/docs/ingestion/signoz-cloud/overview/#endpoint):`https://ingest.<region>.signoz.cloud:443/v1/traces`\n\n→ Your SigNoz`SIGNOZ_INGESTION_KEY`\n\n[ingestion key](https://signoz.io/docs/ingestion/signoz-cloud/keys/)\n\nUsing 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](https://signoz.io/docs/ingestion/cloud-vs-self-hosted/#cloud-to-self-hosted).\n\n**Step 4:** Instrument Azure OpenAI using `OpenAIInstrumentation`\n\nand the configured Tracer Provider\n\n``` js\nconst { registerInstrumentations } = require('@opentelemetry/instrumentation')\nconst { OpenAIInstrumentation } = require('@arizeai/openinference-instrumentation-openai')\n\nregisterInstrumentations({\n  instrumentations: [\n    new OpenAIInstrumentation({\n      tracerProvider: provider,\n    }),\n  ],\n})\n```\n\n📌 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.\n\n**Step 5**: Setup Logs\n\n``` js\nconst { LoggerProvider, BatchLogRecordProcessor } = require('@opentelemetry/sdk-logs')\nconst { OTLPLogExporter } = require('@opentelemetry/exporter-otlp-http')\nconst { Resource } = require('@opentelemetry/resources')\nconst { ATTR_SERVICE_NAME } = require('@opentelemetry/semantic-conventions')\nconst { logs } = require('@opentelemetry/api')\n\nconst logResource = Resource.default({\n  attributes: {\n    [ATTR_SERVICE_NAME]: '<service_name>',\n  },\n})\n\nconst loggerProvider = new LoggerProvider({\n  resource: logResource,\n})\n\nconst logExporter = new OTLPLogExporter({\n  url: process.env.OTEL_EXPORTER_LOGS_ENDPOINT,\n  headers: {\n    'signoz-ingestion-key': process.env.SIGNOZ_INGESTION_KEY,\n  },\n})\n\nloggerProvider.addLogRecordProcessor(new BatchLogRecordProcessor(logExporter))\n\nlogs.setGlobalLoggerProvider(loggerProvider)\n\n// Create a logger instance\nconst logger = logs.getLogger('azure-openai-app')\n```\n\nis the name of your service`<service_name>`\n\n→ SigNoz Cloud endpoint with appropriate`OTEL_EXPORTER_LOGS_ENDPOINT`\n\n[region](https://signoz.io/docs/ingestion/signoz-cloud/overview/#endpoint):`https://ingest.<region>.signoz.cloud:443/v1/logs`\n\n→ Your SigNoz`SIGNOZ_INGESTION_KEY`\n\n[ingestion key](https://signoz.io/docs/ingestion/signoz-cloud/keys/)\n\nUsing 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](https://signoz.io/docs/ingestion/cloud-vs-self-hosted/#cloud-to-self-hosted).\n\n**Step 6**: Setup Metrics\n\n``` js\nconst { MeterProvider, PeriodicExportingMetricReader } = require('@opentelemetry/sdk-metrics')\nconst { OTLPMetricExporter } = require('@opentelemetry/exporter-otlp-http')\nconst { Resource } = require('@opentelemetry/resources')\nconst { ATTR_SERVICE_NAME } = require('@opentelemetry/semantic-conventions')\nconst { metrics } = require('@opentelemetry/api')\nconst { HttpInstrumentation } = require('@opentelemetry/instrumentation-http')\nconst { registerInstrumentations } = require('@opentelemetry/instrumentation')\n\nconst metricResource = Resource.default({\n  attributes: {\n    [ATTR_SERVICE_NAME]: '<service_name>',\n  },\n})\n\nconst metricExporter = new OTLPMetricExporter({\n  url: process.env.OTEL_EXPORTER_METRICS_ENDPOINT,\n  headers: {\n    'signoz-ingestion-key': process.env.SIGNOZ_INGESTION_KEY,\n  },\n})\n\nconst metricReader = new PeriodicExportingMetricReader({\n  exporter: metricExporter,\n  exportIntervalMillis: 10000,\n})\n\nconst meterProvider = new MeterProvider({\n  resource: metricResource,\n  readers: [metricReader],\n})\n\nmetrics.setGlobalMeterProvider(meterProvider)\n\n// Create a meter instance\nconst meter = metrics.getMeter('azure-openai-app')\n\n// Register HTTP instrumentation for outbound HTTP metrics\nregisterInstrumentations({\n  instrumentations: [new HttpInstrumentation()],\n})\n\n// Initialize host metrics for system-level monitoring\nconst hostMetrics = new HostMetrics({\n  meterProvider: meterProvider,\n})\nhostMetrics.start()\n```\n\nis the name of your service`<service_name>`\n\n→ SigNoz Cloud endpoint with appropriate`OTEL_EXPORTER_METRICS_ENDPOINT`\n\n[region](https://signoz.io/docs/ingestion/signoz-cloud/overview/#endpoint):`https://ingest.<region>.signoz.cloud:443/v1/metrics`\n\n→ Your SigNoz`SIGNOZ_INGESTION_KEY`\n\n[ingestion key](https://signoz.io/docs/ingestion/signoz-cloud/keys/)\n\n📌 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\n\n[JavaScript Custom Metrics].\n\nUsing 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](https://signoz.io/docs/ingestion/cloud-vs-self-hosted/#cloud-to-self-hosted).\n\n**Step 7:** Run an example\n\n``` js\nconst OpenAI = require('openai')\n\nconst client = new OpenAI({\n  apiKey: process.env.AZURE_OPENAI_API_KEY,\n  baseURL: 'https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/',\n})\n\nasync function main() {\n  const response = await client.chat.completions.create({\n    model: '<your-model-deployment-name>',\n    messages: [\n      { role: 'system', content: 'You are a helpful assistant' },\n      { role: 'user', content: 'What is SigNoz?' },\n    ],\n    stream: false,\n  })\n\n  console.log(response.choices[0].message.content)\n}\n\nmain()\n```\n\n📌 Note: Before running this code, ensure that you have set the environment variable\n\n`AZURE_OPENAI_API_KEY`\n\nwith your working API key.\n\nView Traces, Logs, and Metrics in SigNoz\n\nYour Azure OpenAI commands should now automatically emit traces, logs, and metrics.\n\nYou should be able to view traces in Signoz Cloud under the traces tab:\n\nWhen 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.\n\nYou 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:\n\nWhen you click on any of these logs in SigNoz, you'll see a detailed view of the log, including attributes:\n\nYou should be able to see Azure OpenAI related metrics in Signoz Cloud under the metrics tab:\n\nWhen you click on any of these metrics in SigNoz, you'll see a detailed view of the metric, including attributes:\n\nDashboard\n\nYou can also check out our custom Azure OpenAI API dashboard [here](https://signoz.io/docs/dashboards/dashboard-templates/azure-openai-dashboard/) 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.", "url": "https://wpnews.pro/news/azure-openai-monitoring-observability-with-opentelemetry", "canonical_source": "https://signoz.io/docs/azure-openai-monitoring", "published_at": "2026-06-11 00:00:00+00:00", "updated_at": "2026-06-12 09:59:49.863323+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "large-language-models", "generative-ai", "ai-infrastructure"], "entities": ["Azure OpenAI", "OpenTelemetry", "SigNoz", "Azure Virtual Networks", "Azure Active Directory", "Azure"], "alternates": {"html": "https://wpnews.pro/news/azure-openai-monitoring-observability-with-opentelemetry", "markdown": "https://wpnews.pro/news/azure-openai-monitoring-observability-with-opentelemetry.md", "text": "https://wpnews.pro/news/azure-openai-monitoring-observability-with-opentelemetry.txt", "jsonld": "https://wpnews.pro/news/azure-openai-monitoring-observability-with-opentelemetry.jsonld"}}