Welcome back to Agent Support—a developer advice column for those head-scratching moments when you’re building an AI agent! Each post answers a real question from the community with simple, practical guidance to help you build smarter agents.

Today’s question comes from a developer that’s right at the beginning of their agent building journey and needs a little help choosing a model.

💬 Dear Agent Support

I’m overwhelmed by all the model options out there. Some are small, some are huge. Some are free, some cost a lot. Some say “multimodal” but I’m not sure if I need that. How do I choose the right model for my agent?

Great question! Model choice is one of the most important design decisions you’ll make. Pick something too small, and your agent may struggle with complex tasks. Go too big, and you could be paying for power you don’t need. Let’s break down the key factors to consider.

🧩 Capabilities vs. Use Case

The first—and most important—question isn’t which model is “best.” It’s what does my agent actually need to do?

Here’s a few angles to think through:

• Input and Output Types
  Will your agent only handle text, or does it need to process other formats like images, audio, or structured data? Models differ in how many modalities they support and in how well they can handle outputs that must follow strict formatting.
• Complexity of Tasks
  Simple, transactional tasks (like pulling information from a document or answering straightforward queries) don’t require the same reasoning depth as tasks that involve planning, multi-step logic, or open-ended creativity. Define the level of reasoning and adaptability your agent needs.
• Control Requirements
  Some agents need highly controlled outputs (think JSON schemas for downstream services), while others benefit from free-form creativity. The degree of control you need (i.e. structured output, function calling, system prompt) should guide model choice.
• Domain Knowledge
  Does your agent operate in a general-purpose domain, or does it need strong understanding of a specific area (like legal, medical, or technical documentation)? Consider whether you’ll rely on the model’s built-in knowledge, retrieval from external sources, or fine-tuning for domain expertise.
• Interaction Style
  Will users interact with the agent in short, direct prompts, or longer, conversational exchanges? Some models handle chat-like, multi-turn contexts better than others, while others excel at single-shot completions.

In short: start by mapping out your agent’s needs in terms of data types, reasoning depth, control, domain, and interaction style. Once you have that picture, it’s much easier to narrow down which models are a genuine fit, and which ones would be mismatched.

⚖️ Performance vs. Cost

Once you know what your agent needs to do, the next trade-off is between performance and cost. Bigger models are often more capable, but they also come with higher latency, usage costs, and infrastructure requirements. The trick is to match “enough performance” to the real-world expectations for your agent.

Here are some factors to weigh:

• Task Complexity vs. Model Size
  If your agent’s tasks involve nuanced reasoning, long-context conversations, or open-ended problem solving, a more capable (and often larger) model may be necessary. On the other hand, for lightweight lookups or structured Q&A, a smaller model can perform just as well, and more efficiently.
• Response Time Expectations
  Latency matters. A model that takes 8–10 seconds to respond may be fine in a batch-processing workflow but frustrating in a real-time chat interface. Think about how quickly your users expect the agent to respond and whether you’re willing to trade speed for accuracy.
• Budget and Token Costs
  Larger models consume more tokens per request, which translates to higher costs, especially if your agent will scale to many users. Consider both per-request cost and aggregate monthly cost based on expected usage volume.
• Scaling Strategy
  Some developers use a “tiered” approach: route simple queries to a smaller, cheaper model and reserve larger models for complex tasks. This can balance performance with budget without compromising user experience. The Azure AI Founder Model Router performs in a similar manner.
• Experimentation Over Assumptions
  Don’t assume the largest model is always required. Start with something mid-range, test it against your use case, and only scale up if you see gaps. This iterative approach often prevents overspending.

At the end of the day, performance isn’t about squeezing the most power out of a model, it’s about choosing the right amount of capability for the job, without paying for what you don’t need.

🔑 Licensing and Access

Even if you’ve found a model that looks perfect on paper, practical constraints around access and licensing can make or break your choice. These considerations often get overlooked until late in the process, but they can have big downstream impacts.

A few things to keep in mind:

• Where the Model Lives
  Some models are only accessible through a hosted API (like on a cloud provider), while others are open source and can be self-hosted. Hosted APIs are convenient and handle scaling for you, but they also lock you into availability, pricing, and rate limits set by the provider. Self-hosting gives you control, but also means managing infrastructure, updates, and security yourself.
• Terms of Use
  Pay attention to licensing restrictions. Some providers limit usage for commercial products, sensitive data, or high-risk domains (like healthcare or finance). Others may require explicit consent or premium tiers to unlock certain capabilities.
• Data Handling and Privacy
  If your agent processes sensitive or user-specific data, you’ll need to confirm whether the model provider logs, stores, or uses data for training. Check for features like “no data retention” modes, private deployments, or enterprise SLAs if compliance is critical.
• Regional Availability
  Certain models or features may only be available in specific regions due to infrastructure or regulatory constraints. This matters if your users are global, or if you need to comply with data residency laws (e.g., keeping data in the EU).
• Support for Deployment Options
  Consider whether the model can be deployed in the way you need—API-based integration, on-prem deployment, or edge devices. If you’re building something that runs locally (say, on a mobile app), an enormous cloud-only model won’t be practical.
• Longevity and Ecosystem
  Models evolve quickly. Some experimental models may not be supported long-term, while others are backed by a stable provider with ongoing updates. Think about how much you want to bet on a model that might disappear in six months versus one with a roadmap you can count on.

Model choice isn’t just about capability and performance, it’s also about whether you can use it under the terms, conditions, and environments that your project requires.

🔍 Exploring Models with Azure AI Foundry

Once you’ve thought through capabilities, performance trade-offs, and licensing, the next step is exploring what’s available to you. If you’re building with Azure, this is where the Azure AI Foundry Models becomes invaluable. Instead of guessing which model might fit, you can browse, filter, and compare options directly, complete with detailed model cards that outline features, intended use cases, and limitations.

Think of the model catalog as your “shopping guide” for models: it helps you quickly spot which ones align with your agent’s needs and gives you the fine print before you commit.

🔁 Recap

Here’s a quick rundown of what we covered:

• Start with capabilities. Match the model’s strengths to the inputs, outputs, and complexity your agent requires.
• Balance performance with cost. Bigger isn’t always better. Pick the right level of capability without overspending.
• Review licensing and access. Make sure the model is available in your region, permitted for your use case, and deployed in the way you need.
• Explore before you build. Use the Azure AI Foundry Model Catalog to filter options, read model cards, and test in the Playground.

📺 Want to Go Deeper?

With so many new models available on an almost daily basis, it can be a challenge to keep up with what’s new! However, our Model Mondays series has you covered! Each week, we bring to you the latest news in AI models.

We also recently launched our brand-new series: Inside Azure AI Foundry. In this series, we dive deep into the latest AI models, tools, and platform features — with practical demos and technical walkthroughs that show you how to integrate them into your workflows. It’s perfect for developers who want to see capabilities in action before deploying them in real projects.

As always remember, your agent doesn’t need the “best” model on paper—it needs the right model for the job it’s designed to do.