Microsoft Azure continues to simplify how developers and enterprises build AI-powered applications. One of the most important additions in this space is Azure AI Foundry (aligned with Azure AI Studio), which provides a unified way to build, manage, and deploy generative AI and machine learning solutions.

At the core of Azure AI Foundry are Foundry Resources.

In this blog post, we’ll cover:

• What Azure Foundry Resources are
• Why they matter
• Key components
• Step-by-step instructions to create an Azure Foundry Resource
• Best practices and common scenarios

What Are Azure Foundry Resources?

Azure Foundry Resources are the foundational Azure resources used by Azure AI Foundry to manage AI workloads such as:

• Large Language Models (LLMs)
• Prompt engineering
• Model deployment
• Evaluation and monitoring
• Secure integration with enterprise data

A Foundry Resource acts as a central AI workspace that connects models, compute, data, and security in one place.

Why Azure Foundry Resources Matter

Traditional AI development often involves stitching together multiple services manually. Azure Foundry Resources simplify this by offering:

• Centralized AI project management
• Built-in security and governance
• Seamless integration with Azure OpenAI models
• Enterprise-grade identity, networking, and compliance
• Faster AI application lifecycle from build to deployment

This makes Foundry Resources ideal for enterprise AI, copilots, and generative AI applications.

Key Components of an Azure Foundry Resource

When you create a Foundry Resource, Azure integrates several services automatically:

• Azure AI Services
• Azure OpenAI (if enabled in your subscription)
• Azure Machine Learning
• Managed identity
• Networking and access controls
• Model catalog and deployments

All of these capabilities are accessible through Azure AI Foundry Studio.

Prerequisites

Before creating a Foundry Resource, make sure you have:

• An active Azure subscription
• Contributor or Owner access on the subscription or resource group
• Access to Azure OpenAI (if you plan to use GPT models)
• A supported Azure region such as East US, West Europe, or Sweden Central

Step-by-Step: How to Create an Azure Foundry Resource

Step 1: Sign in to Azure Portal

Navigate to the Azure Portal and sign in using your Azure credentials.

Step 2: Search for Azure AI Foundry

In the Azure Portal search bar, type Azure AI Foundry and select it from the results.

Step 3: Create a Foundry Resource

On the Azure AI Foundry page, click Create and select Foundry Resource.

Step 4: Configure Basic Details

Provide the following information:

• Subscription: Select your Azure subscription
• Resource Group: Create a new one or select an existing group
• Resource Name: Example ai-foundry-prod-01
• Region: Choose a region that supports Azure AI Foundry and Azure OpenAI

Click Next to continue.

Step 5: Configure Networking

You can choose between:

• Public endpoint (default and easiest)
• Private endpoint (recommended for enterprise and production workloads)

For production environments, enabling a private endpoint and restricting public access is a best practice.

Click Next.

Step 6: Identity and Security

• Managed identity is enabled by default
• Role assignments can be configured later using Azure RBAC

This identity enables secure access to Azure services such as Storage Accounts, Azure OpenAI, and Key Vault.

Click Next.

Step 7: Review and Create

Review all configuration details and click Create.
Deployment typically completes within a few minutes.

Access Azure AI Foundry Studio

After deployment:

1. Open the newly created Foundry Resource
2. Click Launch Azure AI Foundry Studio

From here, you can deploy models, design prompts, build copilots, evaluate outputs, and monitor usage.

Common Use Cases for Azure Foundry Resources

Azure Foundry Resources are commonly used for:

• Enterprise copilots for HR, Finance, and IT
• Document summarization and document intelligence
• Knowledge-base chatbots using RAG patterns
• AI-powered analytics assistants
• Model experimentation, evaluation, and governance

Best Practices

• Use separate Foundry Resources for development, testing, and production
• Enable private networking for sensitive workloads
• Store secrets in Azure Key Vault
• Monitor usage and costs using Azure Monitor
• Use Azure RBAC instead of shared access keys

Final Thoughts

Azure Foundry Resources provide a powerful, secure, and scalable foundation for building enterprise-grade AI solutions on Azure. By simplifying model management, security, and deployment, they allow teams to focus on delivering real business value with AI.

If you are building generative AI applications, copilots, or intelligent platforms, Azure AI Foundry should be one of your first stops.