The model catalog in Azure AI Foundry provides a central repository of models that you can browse to find the right language model for your particular generative AI use case.
Selecting a foundation model for your generative AI app is important as it affects how well your app works. To find the best model for your app, you can use a structured approach by asking yourself the following questions:
- Can AI solve my use case?
- How do I select the best model for my use case?
- Can I scale for real-world workloads?
Let’s explore each of these questions.
Can AI solve my use case?
Nowadays we have thousands of language models to choose from. The main challenge is to understand if there’s a model that satisfies your needs and to answer the question: Can AI solve my use case?
To start answering this question, you need to discover, filter, and deploy a model. You can explore the available language models through three different catalogs:
- Hugging Face: Vast catalog of open-source models across various domains.
- GitHub: Access to diverse models via GitHub Marketplace and GitHub Copilot.
- Azure AI Foundry: Comprehensive catalog with robust tools for deployment.
Though you can use each of these catalogs to explore models, the model catalog in Azure AI Foundry makes it easiest to explore and deploy a model to build you prototype, while offering the best selection of models.
Let’s explore some of the options you need to consider when searching for suitable models.
Choose between large and small language models
First of all, you have a choice between Large Language Models (LLMs) and Small Language Models (SLMs).
- LLMs like GPT-4, Mistral Large, Llama3 70B, Llama 405B, and Command R+ are powerful AI models designed for tasks that require deep reasoning, complex content generation, and extensive context understanding.
- SLMs like Phi3, Mistral OSS models, and Llama3 8B are efficient and cost-effective, while still handling many common Natural Language Processing (NLP) tasks. They’re perfect for running on lower-end hardware or edge devices, where cost and speed are more important than model complexity.
Focus on a modality, task, or tool
Language models like GPT-4 and Mistral Large are also known as chat completion models, designed to generate coherent and contextually appropriate text-based responses. When you need higher levels of performance in complex tasks like math, coding, science, strategy, and logistics, you can also use reasoning models like DeepSeek-R1 and o1.
Beyond text-based AI, some models are multi-modal, meaning they can process images, audio, and other data types alongside text. Models like GPT-4o and Phi3-vision are capable of analyzing and generating both text and images. Multi-modal models are useful when your application needs to process and understand images, such as in computer vision or document analysis. Or when you want to build an AI app that interacts with visual content, such as a digital tutor explaining images or charts.
If your use case involves generating images, tools like DALL·E 3 and Stability AI can create realistic visuals from text prompts. Image generation models are great for designing marketing materials, illustrations, or digital art.
Another group of task-specific models are embedding models like Ada and Cohere. Embeddings models convert text into numerical representations and are used to improve search relevance by understanding semantic meaning. These models are often implemented in Retrieval Augmented Generation (RAG) scenarios to enhance recommendation engines by linking similar content.
When you want to build an application that interacts with other software tools dynamically, you can add function calling and JSON support. These capabilities allow AI models to work efficiently with structured data, making them useful for automating API calls, database queries, and structured data processing.
Specialize with regional and domain-specific models
Certain models are designed for specific languages, regions, or industries. These models can outperform general-purpose generative AI in their respective domains. For example:
- Core42 JAIS is an Arabic language LLM, making it the best choice for applications targeting Arabic-speaking users.
- Mistral Large has a strong focus on European languages, ensuring better linguistic accuracy for multilingual applications.
- Nixtla TimeGEN-1 specializes in time-series forecasting, making it ideal for financial predictions, supply chain optimization, and demand forecasting.
If your project has regional, linguistic, or industry-specific needs, these models can provide more relevant results than general-purpose AI.
Balance flexibility and performance with open versus proprietary models
You also need to decide whether to use open-source models or proprietary models, each with its own advantages.
Proprietary models are best for cutting-edge performance and enterprise use. Azure offers models like OpenAI’s GPT-4, Mistral Large, and Cohere Command R+, which deliver industry-leading AI capabilities. These models are ideal for businesses needing enterprise-level security, support, and high accuracy.
Open-source models are best for flexibility and cost-efficiency. There are hundreds of open-source models available in the Azure AI Foundry model catalog from Hugging Face, and models from Meta, Databricks, Snowflake, and Nvidia. Open models give developers more control, allowing fine-tuning, customization, and local deployment.
Whatever model you choose, you can use the Azure AI Foundry model catalog. Using models through the model catalog meets the key enterprise requirements for usage:
- Data and privacy: you get to decide what happens with your data.
- Security and compliance: built-in security.
- Responsible AI and content safety: evaluations and content safety.
Now you know the language models that are available to you, you should have an understanding of whether AI can indeed solve your use case. If you think a language model would enrich your application, you then need to select the specific model that you want to deploy and integrate.
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