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Microsoft offers several ways to build and run AI agents. This article compares the Azure Functions serverless agents runtime with other Microsoft agent options so that you can choose the option that fits a given agent, or combine more than one.
These options differ mainly in the authoring model, how agents start, where they run and bill, and who the primary user is. They also share building blocks: the developer options can call the same Azure OpenAI and Foundry models, several use Microsoft Agent Framework, and the Model Context Protocol (MCP) provides a common way to share tools across them.
Note
The serverless agents runtime is in preview. Features and configuration can change before general availability. Details for the other options can also change as those products evolve.
Compare the options
The following table summarizes the key differences between the ways you can create and host your agents:
| Option | Authoring model | Hosting and cost | Best suited for |
|---|---|---|---|
| Serverless agents runtime | Markdown .agent.md files plus Python app |
Azure Functions hosted with scale-to-zero | Event-driven agents that react to triggers |
| Foundry prompt agents | Declarative config in portal or code | Managed by Foundry with no agent compute | Agents that need no custom code or hosting |
| Foundry hosted agents | Your code, shipped as a container | Managed by Foundry; container compute | Custom orchestration behind a managed endpoint |
| Copilot Studio | Low-code designer and natural language | Microsoft-hosted; Copilot Credits | Conversational and autonomous agents for Microsoft 365 |
| Microsoft Scout | Natural-language prompts | Local, on the user's device | Personal task automation on a local device |
These options aren't mutually exclusive. For example, a serverless agents runtime app can expose tools through MCP that Foundry agents or Copilot Studio agents consume.