---
name: azure-monitor-opentelemetry-py
description: Configures Azure Monitor Application Insights with OpenTelemetry auto-instrumentation for Python applications in one line.
license: MIT
---

# Azure Monitor OpenTelemetry Distro for Python

One-line setup for Application Insights with OpenTelemetry auto-instrumentation.

## Installation

```bash
pip install azure-monitor-opentelemetry
```

## Environment Variables

```bash
APPLICATIONINSIGHTS_CONNECTION_STRING=InstrumentationKey=xxx;IngestionEndpoint=https://xxx.in.applicationinsights.azure.com/  # Required for all auth methods
AZURE_TOKEN_CREDENTIALS=prod # Required only if DefaultAzureCredential is used in production
```

> **🔑 Auth & lifecycle:** This distro is configured with a connection string by design, but for *AAD-authenticated ingestion* (where supported) prefer `DefaultAzureCredential` via the `credential=` parameter — see the [Azure AD Authentication](#azure-ad-authentication) section. Any Azure SDK clients you create alongside the exporter should be wrapped in `with`/`async with` blocks (and async credentials from `azure.identity.aio` likewise).

## Quick Start

```python
from azure.identity import DefaultAzureCredential
from azure.monitor.opentelemetry import configure_azure_monitor

# Connection string identifies the App Insights resource (read from APPLICATIONINSIGHTS_CONNECTION_STRING env var).
# DefaultAzureCredential authenticates ingestion via Microsoft Entra ID (preferred over instrumentation-key-only auth).
configure_azure_monitor(
    credential=DefaultAzureCredential(),
)

# Your application code...
```

## Explicit Configuration

```python
from azure.identity import DefaultAzureCredential
from azure.monitor.opentelemetry import configure_azure_monitor

# Reads APPLICATIONINSIGHTS_CONNECTION_STRING from env to identify the resource;
# DefaultAzureCredential authenticates ingestion via Microsoft Entra ID.
configure_azure_monitor(
    credential=DefaultAzureCredential(),
)
```

## With Flask

```python
from flask import Flask
from azure.monitor.opentelemetry import configure_azure_monitor

configure_azure_monitor()

app = Flask(__name__)

@app.route("/")
def hello():
    return "Hello, World!"

if __name__ == "__main__":
    app.run()
```

## With Django

```python
# settings.py
from azure.monitor.opentelemetry import configure_azure_monitor

configure_azure_monitor()

# Django settings...
```

## With FastAPI

```python
from fastapi import FastAPI
from azure.monitor.opentelemetry import configure_azure_monitor

configure_azure_monitor()

app = FastAPI()

@app.get("/")
async def root():
    return {"message": "Hello World"}
```

## Custom Traces

```python
from opentelemetry import trace
from azure.monitor.opentelemetry import configure_azure_monitor

configure_azure_monitor()

tracer = trace.get_tracer(__name__)

with tracer.start_as_current_span("my-operation") as span:
    span.set_attribute("custom.attribute", "value")
    # Do work...
```

## Custom Metrics

```python
from opentelemetry import metrics
from azure.monitor.opentelemetry import configure_azure_monitor

configure_azure_monitor()

meter = metrics.get_meter(__name__)
counter = meter.create_counter("my_counter")

counter.add(1, {"dimension": "value"})
```

## Custom Logs

```python
import logging
from azure.monitor.opentelemetry import configure_azure_monitor

configure_azure_monitor()

logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)

logger.info("This will appear in Application Insights")
logger.error("Errors are captured too", exc_info=True)
```

## Sampling

```python
from azure.monitor.opentelemetry import configure_azure_monitor

# Sample 10% of requests
configure_azure_monitor(
    sampling_ratio=0.1
)
```

## Cloud Role Name

Set cloud role name for Application Map:

```python
from azure.monitor.opentelemetry import configure_azure_monitor
from opentelemetry.sdk.resources import Resource, SERVICE_NAME

configure_azure_monitor(
    resource=Resource.create({SERVICE_NAME: "my-service-name"})
)
```

## Disable Specific Instrumentations

```python
from azure.monitor.opentelemetry import configure_azure_monitor

configure_azure_monitor(
    instrumentations=["flask", "requests"]  # Only enable these
)
```

## Enable Live Metrics

```python
from azure.monitor.opentelemetry import configure_azure_monitor

configure_azure_monitor(
    enable_live_metrics=True
)
```

## Azure AD Authentication

```python
from azure.monitor.opentelemetry import configure_azure_monitor
from azure.identity import DefaultAzureCredential, ManagedIdentityCredential

# Local dev: DefaultAzureCredential. Production: set AZURE_TOKEN_CREDENTIALS=prod or AZURE_TOKEN_CREDENTIALS=<specific_credential>
credential = DefaultAzureCredential(require_envvar=True)
# Or use a specific credential directly in production:
# See https://learn.microsoft.com/python/api/overview/azure/identity-readme?view=azure-python#credential-classes
# credential = ManagedIdentityCredential()

configure_azure_monitor(
    credential=credential
)
```

## Auto-Instrumentations Included

| Library | Telemetry Type |
|---------|---------------|
| Flask | Traces |
| Django | Traces |
| FastAPI | Traces |
| Requests | Traces |
| urllib3 | Traces |
| httpx | Traces |
| aiohttp | Traces |
| psycopg2 | Traces |
| pymysql | Traces |
| pymongo | Traces |
| redis | Traces |

## Configuration Options

| Parameter | Description | Default |
|-----------|-------------|---------|
| `connection_string` | Application Insights connection string | From env var |
| `credential` | Azure credential for AAD auth | None |
| `sampling_ratio` | Sampling rate (0.0 to 1.0) | 1.0 |
| `resource` | OpenTelemetry Resource | Auto-detected |
| `instrumentations` | List of instrumentations to enable | All |
| `enable_live_metrics` | Enable Live Metrics stream | False |

## Best Practices

1. **Pick sync OR async and stay consistent.** Do not mix `azure.xxx` sync clients with `azure.xxx.aio` async clients in the same call path. Choose one mode per module.
2. **Flush and shut down providers at process exit.** Call the shutdown/flush APIs (e.g. `tracer_provider.shutdown()`, `meter_provider.shutdown()`, `logger_provider.shutdown()`) at process exit to flush telemetry before the process terminates.
3. **Call configure_azure_monitor() early** — Before importing instrumented libraries
4. **Use environment variables** for connection string in production
5. **Set cloud role name** for multi-service applications
6. **Enable sampling** in high-traffic applications
7. **Use structured logging** for better log analytics queries
8. **Add custom attributes** to spans for better debugging
9. **Use Microsoft Entra authentication** for production workloads