diff --git a/integrations/llms/azure-foundry.mdx b/integrations/llms/azure-foundry.mdx
index c825e0a4..588d91b8 100644
--- a/integrations/llms/azure-foundry.mdx
+++ b/integrations/llms/azure-foundry.mdx
@@ -163,6 +163,7 @@ Required parameters:
 - **Azure Managed ClientID**: Your managed client ID
 - **Azure Foundry URL**: The base endpoint URL for your deployment, formatted according to your deployment type:
   - For AI Services: `https://your-resource-name.services.ai.azure.com/models`
+  - For project-scoped OpenAI v1 deployments: `https://your-resource-name.services.ai.azure.com/api/projects/your-project-name/openai/v1`
   - For Managed: `https://your-model-name.region.inference.ml.azure.com/score`
   - For Serverless: `https://your-model-name.region.models.ai.azure.com`
 
@@ -188,6 +189,7 @@ Required parameters:
 - **Azure Entra Tenant ID**: Your tenant ID
 - **Azure Foundry URL**: The base endpoint URL for your deployment, formatted according to your deployment type:
   - For AI Services: `https://your-resource-name.services.ai.azure.com/models`
+  - For project-scoped OpenAI v1 deployments: `https://your-resource-name.services.ai.azure.com/api/projects/your-project-name/openai/v1`
   - For Managed: `https://your-model-name.region.inference.ml.azure.com/score`
   - For Serverless: `https://your-model-name.region.models.ai.azure.com`
 
@@ -207,6 +209,8 @@ You can Learn more about these [Azure Entra Resources here](https://learn.micros
 
 </Tabs>
 
+For project-scoped OpenAI v1 deployments, enter the full URL ending with `/api/projects/<project-name>/openai/v1`, such as `https://your-resource-name.services.ai.azure.com/api/projects/your-project-name/openai/v1`, exactly as shown in Azure AI Foundry. Portkey appends the request route, such as `/chat/completions`, when sending traffic to the deployment.
+
 ## Adding Multiple Models to Your Azure AI Foundry Provider
 
 You can deploy multiple models through a single Azure AI Foundry provider by using Portkey's custom models feature.