Azure AI Foundry: Getting Started with Microsoft's AI Platform

Everyone's talking about AI. Most people don't know where to start.

Microsoft's answer is Azure AI Foundry — a unified platform for building AI applications. It consolidates what used to be scattered across multiple services into something more coherent.

Let me show you what it actually is and how to get started.

What Is Azure AI Foundry?

Azure AI Foundry is Microsoft's platform for building, deploying, and managing AI applications. Think of it as a workspace that brings together:

• AI models (including Azure OpenAI, open-source models, and custom models)
• Development tools (prompt engineering, testing, evaluation)
• Deployment infrastructure (endpoints, scaling, monitoring)
• Responsible AI features (content safety, evaluation metrics)

It replaced the fragmented experience of jumping between Azure OpenAI Studio, Azure Machine Learning, and various other portals.

Why Should You Care?

If you're an Azure engineer, AI is no longer optional knowledge. Organizations are integrating AI into existing applications, and someone needs to build and maintain that infrastructure.

That someone could be you.

Azure AI Foundry is designed for:

• Developers building AI-powered applications
• Data scientists who want managed infrastructure
• IT professionals who need to deploy and monitor AI workloads

You don't need a PhD in machine learning. You need to understand how the pieces fit together.

Core Concepts

Projects

Everything in AI Foundry lives inside a project. A project is a container for:

• Your AI models and deployments
• Prompt flows and configurations
• Evaluation results
• Connected resources (storage, compute, etc.)

Think of it like a resource group, but specifically for AI workloads.

Model Catalog

The model catalog is where you browse and deploy AI models. It includes:

• Azure OpenAI models (GPT-4, GPT-4o, etc.)
• Open-source models (Llama, Mistral, Phi)
• Microsoft models (Phi-3, Florence)
• Third-party models from partners

You don't have to train anything. You can deploy pre-trained models and customize them with your own data.

Prompt Flow

Prompt flow is where you build AI logic. It's a visual tool for:

• Designing conversation flows
• Chaining multiple AI calls together
• Adding business logic between steps
• Testing and iterating quickly

This is where most of your development work happens.

Deployments

Once you've built something, you deploy it as an endpoint. This gives you:

• A REST API to call from your applications
• Scaling controls
• Usage monitoring
• Cost tracking

Standard Azure deployment patterns apply — managed endpoints or bring your own compute.

Your First AI Foundry Project

Let's build something practical: a customer support assistant that answers questions about your product.

Step 1: Create a Project

1. Go to AI Foundry
2. Click "New project"
3. Give it a name and select a resource group
4. Choose a region (not all regions have all models)

Step 2: Deploy a Model

1. Open the Model Catalog
2. Find GPT-4o (or GPT-4o-mini for lower cost)
3. Click "Deploy"
4. Configure capacity (start small — you can scale later)
5. Wait for deployment (usually a few minutes)

Step 3: Test in Playground

Before writing code, test interactively:

1. Open the Chat playground
2. Select your deployed model
3. Write a system prompt:

You are a helpful customer support assistant for [Your Product].
Answer questions accurately and concisely.
If you don't know the answer, say so — don't make things up.

4. Test with sample questions
5. Iterate on your system prompt until you're happy

Step 4: Add Your Data (Optional)

For domain-specific answers, add your own data:

1. Go to "Data" in your project
2. Upload documents (PDFs, Word docs, text files)
3. Create an index (this enables search over your content)
4. Connect the index to your chat deployment

Now the model can reference your documentation when answering questions.

Step 5: Create a Prompt Flow

For production use, wrap your chat in a prompt flow:

1. Go to "Prompt flow"
2. Create from template: "Chat with your data"
3. Configure:
   • Input handling
   • Model connection
   • Data source connection
   • Response formatting
4. Test the flow end-to-end

Step 6: Deploy as an Endpoint

1. Click "Deploy" from your prompt flow
2. Choose deployment type (managed endpoint is easiest)
3. Configure scaling
4. Get your endpoint URL and API key

Now you can call this from any application.

Integrating with Your Applications

Once deployed, calling your AI endpoint is straightforward:

import requests

endpoint = "https://your-endpoint.inference.ai.azure.com"
api_key = "your-api-key"

response = requests.post(
    f"{endpoint}/score",
    headers={
        "Authorization": f"Bearer {api_key}",
        "Content-Type": "application/json"
    },
    json={
        "question": "How do I reset my password?"
    }
)

print(response.json())

The endpoint handles scaling, authentication, and model management. You just call the API.

Cost Considerations

AI Foundry costs come from multiple places:

• Model inference — Pay per token (input and output)
• Storage — For your data and indexes
• Compute — If using custom deployments
• Search — If using Azure AI Search for data indexing

Start with pay-as-you-go pricing. Only commit to reserved capacity once you understand your usage patterns.

Tip: GPT-4o-mini costs significantly less than GPT-4o. For many use cases, the quality difference is negligible. Start cheap, upgrade if needed.

What AI Foundry Is NOT

Let me set expectations:

• It's not magic. AI models hallucinate, get things wrong, and need careful prompt engineering.
• It's not free. Token costs add up quickly at scale.
• It's not set-and-forget. You need monitoring, evaluation, and continuous improvement.
• It's not a replacement for good architecture. AI is a component, not a solution.

Where to Go Next

Once you're comfortable with basics:

1. Explore different models — Each has different strengths and costs
2. Learn prompt engineering — This skill is the difference between mediocre and great AI applications
3. Implement evaluation — Measure quality systematically, not just by vibes
4. Add responsible AI controls — Content filters, usage policies, monitoring

AI Foundry makes it easier to build AI applications. It doesn't make it easy — that still requires learning, experimentation, and iteration.

But you don't need to be an AI researcher to get started. You need curiosity, an Azure subscription, and willingness to experiment.

The models are available. The tools are ready. The question is whether you'll build something with them.