Joshua Huang recaps the Microsoft Ignite 2025 SMB session, offering developers clear paths to launching their first AI solution on Azure—either by building with Foundry templates or buying a ready-made solution from the Marketplace.

Unlocking Your First AI Solution on Azure: Practical Paths for Developers

Author: Joshua Huang (Microsoft Azure)

Introduction

Developers and technical teams from startups to mid-sized businesses frequently ask how to begin their AI journey. Joshua Huang shares lessons from the Microsoft Ignite 2025 SMB session, highlighting two actionable strategies for getting AI up and running on Azure, no matter your team’s size or experience.

Path 1: Build Quickly with Microsoft Foundry Templates

  • Microsoft Foundry offers customizable templates that minimize setup and let you launch AI solutions such as secure internal chatbots rapidly.
  • Demonstrated tools include:
    • Azure AI Foundry
    • GitHub (via Azure Samples “Get Started with AI Chat” repository)
    • Azure Cloudshell for streamlined deployment
  • Minimal code and infrastructure work is required. Developers gain control over model selection, cost management, and safety filters, plus the option to connect data sources (like SharePoint or Blob Storage).
  • Ideal for teams needing:
    • Extensibility and control
    • Data governance
    • Fast experimentation with room to scale later
  • Reference implementation: Get Started with AI Chat on GitHub

Path 2: Buy a Production-Ready Solution from a Trusted Partner

  • The “buy” approach utilizes Marketplace-issued solutions such as UnifyCloud’s AI Factory.
  • Benefits include:
    • Quick deployment into your Azure tenant
    • Support, management, and best practices included
    • No need to assemble the full AI stack yourself
  • The process as demoed by UnifyCloud’s founder covers navigating the Marketplace, evaluating listings, selecting plans, and deploying the app in a few simple steps.
  • Ideal for teams needing:
    • Production-ready, maintained AI deployments
    • Minimal engineering lift
    • Faster realization of business outcomes
  • Marketplace listing: UnifyCloud’s AI Factory

Why Azure is a Foundation for AI

  • Security and Governance: Azure’s built-in guardrails address security, privacy, and policy requirements by default.
  • End-to-End AI Platform: Azure supports entire AI lifecycles, from models to tools and data integration.
  • Developer-Focused Innovation: Features like agentic DevOps seamlessly integrate AI into software development workflows.

Key Takeaways

  • You don’t need a large budget, deep machine learning expertise, or a dedicated AI engineering team to succeed.
  • Both the “build” and “buy” paths are practical starting points, letting you move at your own pace and according to your team’s needs.
  • The gap between aspiration and operational AI is decreasing rapidly, thanks to tools and partner solutions on Azure.

Session Resources

Closing Thoughts

Joshua Huang emphasizes that with Azure, adopting AI is within reach for developers in business settings of any size. You can build quickly, buy confidently, and start making an impact now—without perfect architecture or a massive budget.

Want to share your progress or have questions? Contact Joshua Huang in the Microsoft Developer Community.

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