The enterprise AI space may seem crowded, but not all “AI platforms” are created equal. Microsoft Azure AI Foundry markets itself as a one-stop shop for AI agents, yet in practice it feels more like a bundle of Azure services pieced together than a true end-to-end workflow builder.

In contrast, StackAI was designed from day one as a full-featured operating system for AI agents, with drag-and-drop workflows, built-in governance, and enterprise-ready flexibility. Below, we break down how the two compare across critical dimensions.

“We explored Azure AI Foundry and quickly realized that Microsoft is really good at showing what their products can do…when you begin to use it, you realize that it is only marketing. Azure AI Foundry could not do nearly what it promised to do and actually it was too difficult to use. We would have needed massive investments in external consultants just to create a simple chatbot or make AI a part of our processes. When we tried StackAI and saw how easy it was to create a working AI agent, we realized that the time from idea to publishing something useful in an enterprise setting was just far quicker.”

Alexander Kristensen

Head of AI, BI, and Intelligence at SDK Freja

TL;DR Comparison

Building Flexibility

Azure Foundry’s workflow builder is extremely basic. There’s little evidence you could design a complete, multi-step enterprise workflow, let alone manage system-wide orchestration.

When we look at the backend “architecture” diagrams, ironically, Foundry workflows suddenly appear needlessly complex, requiring multiple Azure services (AI Search, Application Insights, Container Apps, etc.) just to enable core functions like RAG.

StackAI flips this experience: it’s a true workflow builder, where chaining models, connecting tools, processing files, and exporting outputs are all part of a single drag-and-drop canvas. No hidden complexity, no extra wiring.

Deployment Optionality

Azure AI Foundry is tightly coupled to Azure cloud and Azure services. While Microsoft offers hybrid/on-prem deployment across its stack in other products, we found no clear evidence that Foundry itself can run in a fully on-premise environment without Azure dependencies.

StackAI offers cloud, hybrid, and on-prem deployment options out of the box, giving enterprises in regulated industries full control over where and how their AI runs.

Interfaces and Exports

The Azure Foundry chat interface is limited and unpolished. There’s no clean way to export workflows otherwise except for via API, and the user experience feels like a developer sandbox rather than an enterprise application.

StackAI lets you export interfaces as chatbots, Slack/Teams apps, forms, or APIs, each with customizable branding and access controls. This isn’t just a playground, it’s designed for production.

Retrieval-Augmented Generation (RAG)

Azure supports RAG, but it appears that users have to manually enable it and stitch it together with services like AI Search and Container Apps. This makes adoption clunky and error-prone.

StackAI includes native one-click RAG inside the builder: upload documents, get citations and references instantly, and govern outputs by default. No assembly required.

Integrations and Tool-Calling

Azure Foundry integrates with only a handful of Azure-native tools (Logic Apps, Azure Functions, Bing Search grounding, etc.). While powerful inside Microsoft’s ecosystem, this is a hard limit if you want to call external APIs or integrate with non-Azure enterprise systems.

StackAI offers 100+ native integrations across CRMs, ERPs, knowledge systems, and developer tools, plus the ability to add custom connectors without heavy engineering.

Management Hub and Analytics

Microsoft calls its central dashboard the “Management Center”, but in practice, it’s just a quota-tracking page. You can see which deployments are consuming resources, but you don’t get run-level traces, workflow analytics, or business-level metrics.

StackAI provides full analytics: run histories, error logs, and more on dashboards to monitor usage and outcomes. Instead of watching quotas, you get visibility into impact.

Role-Based Access Control (RBAC) and Governance

Azure AI Foundry offers only three flat roles (User, Project Manager, and Account Owner) with no ability to create custom groups or enforce feature-level access controls. This means governance is tied to project-level visibility, without the flexibility enterprises need for department-specific policies.

In contrast, StackAI provides a governance framework built around fine-grained RBAC and layered controls. Admins can define roles and create groups (for example, Legal, HR, or Capture Teams) and map them directly to workspaces and projects. Every workflow can be locked, versioned, and tied to an approval flow so that builders propose changes and admins approve before publish. At the organizational level, admins can enforce one-click SSO across all interfaces, restrict who can publish workflows, block or enable connectors, and apply additional guardrails. Knowledge bases and integrations are private by default.

Together, these layers go far beyond traditional RBAC, giving enterprises confidence that AI agents can scale securely, remain auditable, and align with compliance requirements.

🔗 To learn more about governance on StackAI, check out this comprehensive report.

Bottom Line

Azure AI Foundry is more of a wrapper for Azure services than a full-featured enterprise AI platform. If your organization is already all-in on Microsoft and just needs lightweight agent prototypes, it can work. But if you need flexible workflows, enterprise governance, multi-deployment options, and true production readiness, StackAI is the clear choice. Get a demo with us if you'd like to find out more about the building platform for enterprise AI.