By Abhishek Kumar — Azure Expert | Technical Architect

In today’s AI-powered world, choosing the right model is just like picking the right tool for a job. If you’re building a Generative AI app on Azure, the Model Catalog in Azure AI Foundry is your one-stop shop—a massive toolbox full of smart models waiting to work for you.

But the real question is:
How do you know which model is right for you?

Let’s break it down step-by-step—in simple language.

🔍 Step 1: Can AI even solve your problem?

Before rushing into AI, ask yourself:

“Can AI actually help in solving my use case?”

There are thousands of models available today. So, how do you find the one that fits your need? Azure AI Foundry brings you an easy-to-navigate Model Catalog that combines top model sources like:

• Hugging Face – A hub of open-source models for all types of tasks
• GitHub – Models from GitHub Copilot and the Marketplace
• Azure AI Foundry – The best-in-class curated and deployable models

You can browse, filter, and deploy right from Azure’s catalog without switching platforms. It’s designed to help you go from idea to prototype, fast.

🏗️ Step 2: Pick the Right Model Type

There are two major types of models to choose from:

🔹 Large Language Models (LLMs)

These are like super-brains. Examples: GPT-4, Mistral Large, Llama3 70B
They’re great for:

• Writing long, smart content
• Answering complex questions
• Coding, reasoning, and planning

🔹 Small Language Models (SLMs)

Think of these as fast, lightweight assistants. Examples: Phi-3, Mistral OSS, Llama3 8B
Perfect for:

• Basic NLP tasks
• Edge devices and budget-friendly apps

Choose LLMs when accuracy and intelligence are key.
Choose SLMs when speed and cost matter more.

🧰 Step 3: Pick Based on Task and Tool

Different models are built for different jobs. Some ideas:

• ✍️ Text Generation – GPT-4, Mistral Large (good for chatbots, summaries)
• 🧠 Reasoning – DeepSeek-R1, o1 (great for math, coding, logic)
• 🎨 Image Generation – DALL·E 3, Stability AI (turn text into images)
• 👁️‍🗨️ Multimodal – GPT-4o, Phi-3 Vision (understand both text and images)
• 🔍 Search & RAG – Ada, Cohere (use embeddings to power smart searches)
• 🧩 Function Calling/JSON – Ideal for building interactive AI tools like travel planners, expense trackers, or dynamic APIs

🌍 Step 4: Go Local or Global – Use Region/Domain Specific Models

Sometimes, a specialized model does the job better:

• Core42 JAIS: Arabic language support
• Mistral Large: Better performance for European languages
• Nixtla TimeGEN-1: Time-series forecasting for financial, supply chain apps

Use these when you’re targeting a specific region, language, or industry.

🔐 Step 5: Open vs Proprietary – What’s Best for You?

There are two types of models:

• Proprietary Models: Closed-source, high performance, perfect for enterprise needs (like GPT-4, Cohere Command R+)
• Open-Source Models: More control, flexible, cost-efficient (like Hugging Face models, Meta’s Llama)

💡 Tip: Azure AI Foundry supports both, so you can choose what’s best for your use case and budget.

✅ Step 6: Filter for Precision and Performance

Ask yourself:

• Does the model produce accurate and relevant results? (Precision)
• Is it trained specifically for my task, or is it generic?
• Can I fine-tune it for better results?

Models are either:

• Base models (general-purpose)
• Fine-tuned models (custom-trained for specific needs)

You can also compare models based on benchmarks like:

Metric	What it Measures
Accuracy	How correct the answers are
Coherence	How smoothly the response flows
Fluency	Grammar and readability
Groundedness	Does the answer stick to facts?
GPT Similarity	How close it is to a good human answer
Cost	How much it costs per use (tokens)

Use manual evaluation (like testing yourself), or automated evaluation tools for more structured feedback.

🚀 Step 7: Can It Scale for Real Life?

Once you’ve tested your model and it works in a prototype, ask:

• 📦 Where do I deploy the model—cloud, edge, or serverless?
• 📊 How do I monitor it over time?
• 💬 How do I manage prompts to get better answers?
• 🔁 How do I update models, data, and code regularly?

Azure AI Foundry provides tools for GenAIOps – a lifecycle approach to build, test, monitor, and scale generative AI apps with both visual and code-first experiences.

💡 Final Thoughts:

Think of the Azure AI Foundry Model Catalog as a huge online shopping mall—but for AI models. Whether you’re building a chatbot, generating creative content, analyzing data, or designing a multilingual assistant, the catalog helps you filter down your choices based on your real-world needs.

So next time you’re stuck thinking,

“Which AI model should I use?”

Just walk through these steps—and you’ll find the right fit.