Azure OpenAI LLM analytics installation

Install the SDKs

Required

Setting up analytics starts with installing the PostHog and OpenAI SDKs.

pip install posthog openai
npm install @posthog/ai posthog-node openai

Initialize PostHog and Azure OpenAI client

Required

We call Azure OpenAI through PostHog's AzureOpenAI wrapper to capture all the details of the call. Initialize PostHog with your PostHog project API key and host from your project settings, then pass the PostHog client along with your Azure OpenAI config (the API key, API version, and endpoint) to our AzureOpenAI wrapper.

from posthog.ai.openai import AzureOpenAI
from posthog import Posthog

posthog = Posthog(
    "<ph_project_api_key>",
    host="https://us.i.posthog.com"
)

client = AzureOpenAI(
    api_key="<azure_openai_api_key>",
    api_version="2024-10-21",
    azure_endpoint="https://<your-resource>.openai.azure.com",
    posthog_client=posthog
)
import { AzureOpenAI } from '@posthog/ai'
import { PostHog } from 'posthog-node'

const phClient = new PostHog(
  '<ph_project_api_key>',
  { host: 'https://us.i.posthog.com' }
);

const client = new AzureOpenAI({
  apiKey: '<azure_openai_api_key>',
  apiVersion: '2024-10-21',
  endpoint: 'https://<your-resource>.openai.azure.com',
  posthog: phClient,
});

// ... your code here ...

// IMPORTANT: Shutdown the client when you're done to ensure all events are sent
phClient.shutdown()

Note: This also works with the AsyncAzureOpenAI client.

Proxy note

These SDKs do not proxy your calls. They only fire off an async call to PostHog in the background to send the data. You can also use LLM analytics with other SDKs or our API, but you will need to capture the data in the right format. See the schema in the manual capture section for more details.

Call Azure OpenAI

Required

Now, when you call Azure OpenAI, PostHog automatically captures an $ai_generation event. You can also capture or modify additional properties with the distinct ID, trace ID, properties, groups, and privacy mode parameters.

response = client.chat.completions.create(
    model="<your-deployment-name>",
    messages=[
        {"role": "user", "content": "Tell me a fun fact about hedgehogs"}
    ],
    posthog_distinct_id="user_123", # optional
    posthog_trace_id="trace_123", # optional
    posthog_properties={"conversation_id": "abc123", "paid": True}, # optional
    posthog_groups={"company": "company_id_in_your_db"},  # optional
    posthog_privacy_mode=False # optional
)

print(response.choices[0].message.content)
const completion = await client.chat.completions.create({
    model: "<your-deployment-name>",
    messages: [{ role: "user", content: "Tell me a fun fact about hedgehogs" }],
    posthogDistinctId: "user_123", // optional
    posthogTraceId: "trace_123", // optional
    posthogProperties: { conversation_id: "abc123", paid: true }, // optional
    posthogGroups: { company: "company_id_in_your_db" }, // optional
    posthogPrivacyMode: false // optional
});

console.log(completion.choices[0].message.content)

Notes:

This works with responses where stream=True.

If you want to capture LLM events anonymously, don't pass a distinct ID to the request.

See our docs on anonymous vs identified events to learn more.

You can expect captured $ai_generation events to have the following properties:

Property	Description
`$ai_model`	The specific model, like `gpt-5-mini` or `claude-4-sonnet`
`$ai_latency`	The latency of the LLM call in seconds
`$ai_time_to_first_token`	Time to first token in seconds (streaming only)
`$ai_tools`	Tools and functions available to the LLM
`$ai_input`	List of messages sent to the LLM
`$ai_input_tokens`	The number of tokens in the input (often found in response.usage)
`$ai_output_choices`	List of response choices from the LLM
`$ai_output_tokens`	The number of tokens in the output (often found in `response.usage`)
`$ai_total_cost_usd`	The total cost in USD (input + output)
[...]	See full list of properties

Verify traces and generations
Recommended
Confirm LLM events are being sent to PostHog
Let's make sure LLM events are being captured and sent to PostHog. Under LLM analytics, you should see rows of data appear in the Traces and Generations tabs.

Check for LLM events in PostHog

Next steps

Recommended

Now that you're capturing AI conversations, continue with the resources below to learn what else LLM Analytics enables within the PostHog platform.

Resource	Description
Basics	Learn the basics of how LLM calls become events in PostHog.
Generations	Read about the `$ai_generation` event and its properties.
Traces	Explore the trace hierarchy and how to use it to debug LLM calls.
Spans	Review spans and their role in representing individual operations.
Anaylze LLM performance	Learn how to create dashboards to analyze LLM performance.

Azure OpenAI LLM analytics installation

Contents

Install the SDKs

Initialize PostHog and Azure OpenAI client

Call Azure OpenAI

Verify traces and generations

Next steps

Community questions

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