Azure Foundry: What Everyone’s Talking About

Last Updated on May 2, 2025 by Arnav Sharma

If you’ve been scrolling through LinkedIn or reading tech updates lately, you’ve probably seen the term “Azure Foundry” pop up again and again. It’s being discussed by AI leaders, cloud architects, developers, and pretty much everyone in tech.

But what exactly is Azure Foundry? Why is it suddenly everywhere?

Azure Foundry Isn’t Just One Thing

The important thing to understand upfront is that “Azure Foundry” does not refer to a single product or service officially released by Microsoft.
Instead, the buzz around Azure Foundry is actually pointing to two very different things:

Azure AI Foundry, which is Microsoft’s newly rebranded and expanded platform for building and managing enterprise AI applications and intelligent agents.
Cloud Foundry on Azure, the established practice of running the open-source Cloud Foundry platform on Azure’s infrastructure for traditional application deployment.

Both have “Foundry” in the name, but they solve completely different problems.
Today, the majority of the excitement and a good chunk of the confusion, is being driven by Azure AI Foundry.

The Rise of Azure AI Foundry

Azure AI Foundry is a major new platform from Microsoft designed to bring enterprise-scale AI application development under one roof.
It is not just another way to deploy AI models. It is a full system that helps companies build, customize, test, secure, and deploy AI-powered products.

Here’s what Azure AI Foundry actually includes:

1. Access to a Huge Library of AI Models

Enterprises get access to over 1800 models sourced from Microsoft Research, OpenAI, Meta (Llama 3, Llama 4), DeepSeek, Mistral, Phi, Hugging Face, and more.
Organizations can fine-tune or adapt these models to specific use cases without starting from scratch.
Example: A legal tech company could pick a language model fine-tuned for summarization tasks to build an internal legal research assistant.

2. Visual Development with Prompt Flow

Prompt Flow offers a visual, low-code way to design complex AI workflows.
Developers can easily connect inputs, outputs, logic conditions, databases, APIs, and AI models together through an intuitive interface.
Example: A financial services company could use Prompt Flow to classify incoming client inquiries, generate personalized responses, and escalate complex cases automatically — without custom backend code.

3. Building Intelligent AI Agents

Azure AI Foundry enables building intelligent agents that go beyond simple Q&A tasks.
These agents can reason, plan actions, interact with external systems, and collaborate as part of multi-agent systems.
Example: A customer service platform could build an agent that fetches a customer’s subscription history, offers a payment extension, and triggers an update to the CRM — all autonomously through agent workflows.

4. Safe and Responsible AI Built-in

Integrated safety tools allow enterprises to evaluate, benchmark, and validate model behavior before production deployment.
Includes red teaming capabilities through Microsoft’s PyRIT framework to simulate attacks and uncover vulnerabilities.
Example: A healthcare startup developing a virtual assistant could use these safety tests to ensure the agent does not hallucinate risky medical advice or share confidential patient information.

5. Flexible Deployment Options

AI applications can be deployed through multiple options based on need and scale.
Supported deployments include serverless APIs, Azure Kubernetes Service (AKS) clusters, Azure Functions for event-driven triggers, or hybrid cloud environments using Azure Arc.
Example: A logistics company could deploy a fleet optimization agent inside Azure for headquarters management, while pushing lighter versions to edge locations like warehouses.

6. Developer-Friendly Environment

Offers SDKs for Python, .NET, JavaScript, and tools for CLI-based automation.
Integrates natively with GitHub Actions, Azure DevOps pipelines, and Visual Studio Code for continuous delivery.
Example: A retail company building an AI-based shopping assistant could automate deployments from its GitHub repo, using CI/CD pipelines to trigger model refreshes and monitor real-time metrics in Azure.

Cloud Foundry on Azure: The Older, Still Relevant Foundry

While Azure AI Foundry is stealing the spotlight right now, the idea of a “Foundry” running on Azure isn’t exactly new.
For years, companies have been running Cloud Foundry, an open-source PaaS (Platform as a Service), on Azure’s cloud infrastructure.

Let’s quickly break down how that works — and why it still matters.

1. Easy App Deployment (cf push and forget)

Cloud Foundry’s magic is all about simplicity. Developers just push code using cf push, and everything else — the servers, the containers, the network — gets handled automatically.
No worrying about virtual machines, networking, or Kubernetes clusters underneath.

For example, an e-commerce company with three apps (like a payment service, a shopping cart, and a recommendation engine) could get all of them live without touching a single load balancer or server dashboard. Just write code, push it, done.

2. Behind the Scenes: BOSH and Azure Integration

Enterprises used a tool called BOSH to deploy and manage Cloud Foundry environments.
Microsoft even created a Cloud Provider Interface (CPI) so BOSH could talk directly to Azure’s APIs and manage VMs, storage, and networking.

Picture a major bank that already built dozens of microservices on Cloud Foundry. Instead of rebuilding everything from scratch to move to Azure, they just shifted their Cloud Foundry foundation onto Azure VMs — no disruption to developer workflows.

3. The Modern Twist: Korifi + Kubernetes

Fast forward to today: the Cloud Foundry community is moving toward Kubernetes with a project called Korifi.
Korifi lets you install a Cloud Foundry-style developer experience directly on Azure Kubernetes Service (AKS).
Same cf push simplicity — but now powered by modern Kubernetes under the hood.

Say a healthcare company wants to modernize its platforms, but its developers are used to cf push workflows. With Korifi on AKS, they can update the infrastructure without needing every developer to become a Kubernetes expert overnight.

Why the Buzz Around Azure Foundry Right Now?

So why is “Azure Foundry” popping up everywhere all of a sudden?

Turns out, it is a mix of smart branding, real platform innovation, and — let’s be honest — a little natural confusion.

1. The Name Game: Azure AI Studio Became Azure AI Foundry

Microsoft rebranded Azure AI Studio into Azure AI Foundry, giving it a bigger, bolder identity.
“Foundry” makes it sound serious — like a place where AI solutions are built, not just experimented with.

It feels way more aligned to enterprise-grade ambitions than something called a “studio.”

2. Everyone Wants AI (And Fast)

From healthcare to banking to manufacturing, every industry is trying to figure out how to use AI — responsibly, securely, and at scale.
Azure AI Foundry promises a smoother, safer way to get there.

Think of a hospital network building a private AI system for patient triage. With Foundry, they can find a model, customize it, test it for safety, and deploy it securely without stitching together ten different services themselves.

3. Microsoft Is Pushing Hard (And Fast)

New models like GPT-4 Turbo, DeepSeek, and Llama 3 are dropping into the platform constantly.
Foundry now ties into Azure OpenAI Service, Cosmos DB, API Management, and even NVIDIA microservices for fast AI deployments.
Plus, it is showing up in every keynote, blog, event, and partnership announcement you see from Microsoft.

The message is clear: Foundry is not a side project. It is a centerpiece.

4. Old Name Meets New Hype

Some of the buzz is just natural curiosity.
People who knew Cloud Foundry hear “Azure Foundry” and think — wait, is this the same thing?
Others dive in and realize it’s a whole new world — especially focused on AI, agents, governance, and secure deployments.

It’s a fun collision between past and future.

The Bottom Line: Two Foundries, Two Different Stories

When someone says “Azure Foundry” today, chances are they mean Azure AI Foundry — the big new AI platform helping companies build responsible, scalable AI solutions.

But it is still worth remembering that Cloud Foundry on Azure played a huge role in making cloud development easier, and with Korifi, it is still evolving into the Kubernetes era.

In simple terms:

Cloud Foundry on Azure = Simplifying app deployment across clouds.
Azure AI Foundry = Building the next generation of safe, powerful, enterprise-grade AI.

Both made life easier for developers.
Both let businesses move faster.
But today, the real spotlight belongs to AI — and the massive opportunities Azure AI Foundry is unlocking.

Understanding this distinction means you are not just keeping up with the hype — you are seeing the shape of the future.

TL;DR

“Azure Foundry” can mean two things, but in 2025, it’s mostly about Azure AI Foundry, ie. Microsoft’s platform for building, testing, and scaling enterprise AI applications.

Cloud Foundry on Azure is the older approach for simplifying app deployment, but today, when people mention Azure Foundry, they’re usually talking about the future of AI, not traditional cloud apps.