Skip to main content

Azure AI Foundry Integration

Azure AI Foundry (formerly Azure AI Studio) is Microsoft's platform for building and deploying AI applications. LangGuard integrates with Azure AI Foundry to discover AI resources, deployments, and ingest traces.

Overview​

The Azure AI Foundry integration enables LangGuard to:

  • Discover AI resources — Projects, deployments, and endpoints in your Azure subscription
  • Ingest traces from Azure AI Foundry applications
  • Track model deployments and their usage
  • Monitor performance — Latency, token usage, and error rates

Prerequisites​

  • An Azure subscription with AI Foundry resources
  • Azure AD application (service principal) with appropriate permissions
  • LangGuard admin role

Setup​

Step 1: Create an Azure AD Application​

  1. Navigate to the Azure Portal
  2. Go to Azure Active Directory > App registrations
  3. Click New registration
  4. Name it "LangGuard Integration"
  5. After creation, note the Application (client) ID and Directory (tenant) ID
  6. Under Certificates & secrets, create a new Client secret and copy the value

Step 2: Assign Permissions​

Grant the service principal read access to your AI Foundry resources:

  1. Navigate to your Azure AI Foundry resource or resource group
  2. Go to Access control (IAM)
  3. Click Add role assignment
  4. Assign the Reader role to your LangGuard application

Step 3: Add Integration in LangGuard​

  1. Navigate to Integrations in the sidebar
  2. Click Add Integration
  3. Select AI Platforms > Azure AI Foundry
  4. Enter:
    • Name: A friendly name (e.g., "Production Azure AI")
    • Subscription ID: Your Azure subscription ID
    • Tenant ID: Your Azure AD tenant ID
    • Client ID: The application (client) ID
    • Client Secret: The client secret value
  5. Click Test Connection
  6. Click Save

What Gets Captured​

AI Resources​

LangGuard discovers and catalogs your Azure AI Foundry resources:

ResourceDetails Captured
ProjectsName, region, status
DeploymentsModel name, version, SKU, endpoint
EndpointsURL, authentication method, traffic split

Traces​

When traces are enabled in your Azure AI applications:

FieldDescription
OperationThe AI operation performed
ModelThe deployed model used
Input/Output TokensToken counts
LatencyResponse time
StatusSuccess or error

Troubleshooting​

Authentication Failed​

  1. Verify the Client ID, Client Secret, and Tenant ID are correct
  2. Check that the client secret hasn't expired
  3. Ensure the service principal has the Reader role on the subscription or resource group

No Resources Discovered​

  1. Confirm AI Foundry resources exist in the specified subscription
  2. Verify the service principal has read access to the correct resource group
  3. Check that the Subscription ID is correct

No Traces Appearing​

  1. Ensure tracing is enabled in your Azure AI Foundry applications
  2. Verify the Application Insights instance is connected
  3. Check the time range in LangGuard

Next Steps​