By Abhishek Kumar — Azure Expert | Technical Architect

You’ve picked the perfect AI model for your Generative AI application. Great!
Now comes the next big step: Deploying that model so your app can use it.

But what does “deploying a model” really mean? And why is it necessary?

Let’s explore this in simple terms, and see how Azure AI Foundry makes it easy.

💡 Why Do You Need to Deploy a Model?

Imagine you’ve built a smart assistant for your app, like a chatbot. To respond to user questions, that assistant needs to talk to a language model.

But that model lives in the cloud. So, how does your app talk to it?

That’s where deployment comes in. When you deploy a model, you’re basically putting it online and giving it an address (URL) where your app can find it and send messages.

This address is called an endpoint.

Here’s what happens when a user asks something in your app:

1. 🧑‍💻 The user asks a question (like: “What’s the weather today?”)
2. 🌐 Your app sends this to the model’s API endpoint
3. 🧠 The model processes the question
4. 📩 A smart, relevant answer is returned to your app
5. 👀 The user sees the result instantly

In short:
Deploying = Making your AI model available for real-time use.

🏗️ How to Deploy a Model Using Azure AI Foundry

Azure AI Foundry offers 3 flexible deployment options, depending on your use case and model type.

Let’s explore them one by one:

✅ 1. Standard Deployment

Best for: Most Azure AI Foundry models (like GPT-4, Mistral, Cohere)
Where it’s hosted: Inside your own Azure AI Foundry project resource
Billing: Based on tokens (you pay per number of words or characters used)

Example: You build a chatbot inside your internal company portal and deploy a GPT-4 model using Standard Deployment. You get full control and easy integration.

✅ 2. Serverless Compute

Best for: Foundry Models with pay-as-you-go usage
Where it’s hosted: Microsoft-managed serverless infrastructure
Billing: Also token-based, but you don’t need to manage any servers

Example: You’re prototyping a public-facing AI assistant that needs to scale instantly. You don’t want to manage infrastructure, so Serverless is perfect.

✅ 3. Managed Compute

Best for: Open-source or custom-trained models
Where it’s hosted: Dedicated virtual machines managed by Azure
Billing: Based on compute time, not just tokens
More power = more cost

Example: You fine-tuned a multilingual legal assistant model for your law firm. Managed Compute gives you the performance and customization you need.

💰 Compare All Three Deployment Types Quickly:

Deployment Type	Supported Models	Hosting Location	Billing Type
Standard	Azure AI Foundry + OpenAI models	Your project resource	Token-based
Serverless	Foundry Models (pay-as-you-go)	Microsoft-managed serverless	Token-based
Managed Compute	Open-source + custom models	Managed VMs in your hub	Compute-based

🛠️ What Happens After You Deploy a Model?

Once deployed, your model can now be used just like a smart service.

Here’s a real-world example of how it works in a chat app:

1. You enter: “Summarize this article”
2. The app sends your message to the deployed model’s endpoint
3. The model returns a short summary
4. You see the summarized content on screen

🧩 It’s seamless, invisible, and real-time—thanks to deployment.

🌍 Scaling Up to Real-World Workloads

Now imagine your app becomes popular—lots of users, lots of questions.
Can your model handle it?

This is where scalability comes in. Azure AI Foundry helps you scale by:

• Letting you switch between deployment types as you grow
• Monitoring usage and performance
• Managing model lifecycles (updates, fine-tuning, security)

This flexibility ensures that you can start small and scale big.

🧠 In Summary: Why Deployment Matters

If you want your AI model to work with your application, deployment is non-negotiable.

Just like you can’t use an app without installing it, you can’t use a language model without deploying it.
Azure AI Foundry gives you multiple deployment styles, so you can choose the one that’s right for your goals, your users, and your budget.

🎯 Abhishek’s Take:

• 🛰️ Deploying a model means putting it online via an endpoint so your app can use it
• 🔧 Azure AI Foundry offers:
  • Standard Deployment (token-based, simple)
  • Serverless (no infrastructure to manage)
  • Managed Compute (powerful VMs for advanced models)
• 🧩 Once deployed, your app talks to the model using API requests
• ⚙️ You can monitor and scale as your app grows

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