> For the complete documentation index, see [llms.txt](https://docs.aidi.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.aidi.ai/mosaic/get-started/llm-configuration/azure-ai-foundry.md).

# Azure AI Foundry

Recommended path. Deploy a model in your Azure AI Foundry project and connect it to Mosaic.

Mosaic connects to a model you've deployed in your own Azure AI Foundry project. Inference happens in your Azure subscription, your billing, your governance.

## Prerequisites

* An Azure subscription with permission to create AI Foundry resources
* Familiarity with the [Azure AI Foundry portal](https://ai.azure.com/)
* A Tenant Admin account in Mosaic

## Steps

{% stepper %}
{% step %}
**Create or open an Azure AI Foundry project**

In the [Azure AI Foundry portal](https://ai.azure.com/), create a project (or open an existing one). The project is the unit Mosaic will connect to.

If you don't already have a project: **+ Create project** → choose a hub → name it (e.g., `mosaic-inference`) → create.
{% endstep %}

{% step %}
**Deploy a model**

Inside the project: **Models + endpoints** → **Deploy model** → pick a model. Mosaic works well with:

* **Claude (Sonnet or Opus)** — best for the agent flows used by Variance Commentary, Sales Pulse, etc.
* **GPT-4 / GPT-4o** — strong general-purpose default
* **Mistral / Llama** — open-weight options if your governance prefers them

Give the deployment a name you'll recognise (e.g., `claude-sonnet-prod`).
{% endstep %}

{% step %}
**Copy the endpoint URL and key**

Once the deployment is running:

* **Endpoint URL** — visible on the deployment detail page (e.g., `https://<project>.openai.azure.com/`)
* **API Key** — under **Keys and endpoint** for the project

Copy both. The API key is sensitive — do not share or commit to source control.
{% endstep %}

{% step %}
**Connect Mosaic to your Foundry endpoint**

In Mosaic:

1. **Admin → AI Configuration**
2. **Add provider → Azure AI Foundry**
3. Paste:
   * **Endpoint URL**
   * **API Key**
   * **Deployment name** (the one from step 2)
4. Click **Test connection** — Mosaic sends a tiny test prompt and verifies the response
5. Click **Save**

Mosaic now uses your Foundry endpoint for all AI inference across the tenant.
{% endstep %}

{% step %}
**Verify**

Open a Mosaic chat and ask any question. The agent reasoning should stream as expected. Check **Admin → AI Sessions** — the model name shown for the new session should match your Foundry deployment name.
{% endstep %}
{% endstepper %}

## Recommended Foundry settings

* **Content filter**: Microsoft's default (Strict / Default / Off). Strict reduces false positives from analyst questions about sensitive topics; Default is the safer baseline for most tenants.
* **TPM (tokens per minute) quota**: start at 100 K TPM and scale based on usage. Foundry shows usage trends in its monitoring dashboards.
* **Region**: pick the region closest to your users. Mosaic's app servers are in India; latency is acceptable from any global Foundry region but lower from nearby ones.

## Switching models

You can change the deployed model in your Foundry project at any time. Mosaic re-uses whatever the deployment-name resolves to. There's no Mosaic-side switch; just update Foundry.

## What's next

[Mosaic App Configurations →](/mosaic/get-started/powerbi-configuration/mosaic-app-configurations.md)