Azure AI Foundry vs. Google Vertex AI: Comparison Guide [2026]

Ayswarrya G

Contributing Writer, Data Engineering & Metadata

Updated:09/11/2026

Published:09/11/2026

13 min read

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Key takeaways

Azure AI Foundry is Microsoft's platform for building and deploying AI apps and agents across Azure and Microsoft 365.
Vertex AI evolved into the Gemini Enterprise Agent Platform in April 2026, distinct from the separate Gemini Enterprise app.
Each platform governs AI assets only within its own cloud, so multi-cloud setups still lack one shared source of context.
Neither platform provides governed business context or a cross-platform asset registry.

Azure AI Foundry vs. Google Vertex AI: what's the real difference?

Azure AI Foundry, now Microsoft Foundry, is Microsoft's platform for building and deploying AI apps and agents across Azure and Microsoft 365. Google Vertex AI is Google Cloud's machine learning platform, now evolved into the Gemini Enterprise Agent Platform for Gemini-native generative and agentic AI. Each platform governs AI assets only within its own cloud, so a multi-cloud enterprise still lacks one shared source of context across both.

What the decision comes down to:

Architecture Foundry is a control plane and resource hierarchy; Vertex AI organizes around agents across build, scale, govern, and optimize
Models and tools both reach most frontier models and support MCP, but each keeps its RAG and tool catalog inside its own cloud
The real gap neither platform ships a cross-cloud context layer, so a multi-cloud enterprise still needs one shared source of context

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Microsoft Foundry (formerly Azure AI Foundry) and the Gemini Enterprise Agent Platform (formerly Google Vertex AI, evolved into this developer platform at Google Cloud Next 2026) are agent platforms for building agents and agentic applications.

The main difference comes down to the cloud each platform lives on and each vendor’s starting point. Start from your existing cloud commitment and preferred model families, since both platforms cover model access, deployment, governance, and agent orchestration.

Here’s where the two platforms actually diverge:

Core design differences
Ecosystem of services
Models these platforms can run
Process of building, deploying, and maintaining agents
How agents retrieve and use context

Azure AI Foundry vs. Google Vertex AI: at a glance

Dimension	Azure AI Foundry	Google Vertex AI
Official name	Renamed Microsoft Foundry in 2025	Evolved into the Gemini Enterprise Agent Platform at Google Cloud Next 2026. Not to be confused with Gemini Enterprise, the separate business-user app that Google Agentspace became in October 2025
What is it?	Microsoft’s app and agent platform on Azure, launched at Ignite in November 2024 and rebranded Microsoft Foundry a year later	Google Cloud’s ML and generative AI platform, generally available since May 2021 and evolved into the Gemini Enterprise Agent Platform in April 2026
Design organization	Control plane, resource hierarchy	Build, scale, govern, optimize
Entry point	Azure AI Foundry resource and projects	Agents
Agent definition method	No-code prompt agents and hosted agents	No-code agents with Google Agent Studio, and hosted agent-equivalent with Google’s ADK
Agent runtime	Prompt agents run on Foundry’s fully managed runtime; hosted agents run in micro VMs on per-session sandboxes	Both agent types run on Google’s managed runtime, Agent Runtime (formerly Agent Engine)
Agent development	Build prompt agents from the Foundry portal or the Foundry SDK. Build hosted agents using an agent framework like LangGraph or Microsoft Agent Framework	Build agents using the open-source and model-agnostic ADK by Google or any other open-source framework of your choice
Tools catalog	Built-in tools like file search, code interpretation, etc. Add new tools with remote MCP. Foundry also has its RAG solution in Foundry IQ	Built-in tools, such as Grounding with Google Search, Vertex AI Search, RAG Engine, and code execution. You can also register your own APIs and MCP servers
Models used	Most frontier models, and others via Hugging Face Hub	Most frontier models, and any models published in the Model Garden
Multi-agent support	Only A2A for calling remote agents	Any framework built for multi-agent orchestration and using A2A is supported
Context from enterprise data sources	Context grounding with Bing Search, Foundry IQ, and Fabric	Context grounding via Google Search, along with access to Cloud Storage, Google Drive, BigQuery, and the Apigee API Hub
Context from RAG sources	Foundry IQ knowledge bases built on Azure AI Search	Vertex AI Search, RAG Engine, and Vector Search
Knowledge graph, semantic layer, ontology, glossary	No built-in graph, semantic layer or ontology within Azure AI Foundry currently	No built-in graph, semantic layer, or ontology
Agent memory and session state	Supports short-term, long-term, and procedural memory (fully managed)	Separates session memory from cross-session memory
Best fit	Enterprises already standardized on Azure and Microsoft 365	Enterprises on Google Cloud that want native Gemini models, and direct BigQuery data integration

How do Azure AI Foundry and Google Vertex AI differ in their core design?

Azure AI Foundry (now known as Microsoft Foundry) is designed as a control plane for agents, models, connections, and other services that interact with agents. It follows a resource hierarchy, with a Foundry resource at the top that handles security and deployments, and Foundry projects as the isolation boundary.

Google Vertex AI (now evolved into the Gemini Enterprise Agent Platform) handles the organization slightly differently, with everything structured around agents across four pillars: build, scale, govern, and optimize.

While the design is slightly different, the methods of creating and deploying agents are similar. Here’s a quick comparison of the core design features across the two platforms.

Design feature	Azure AI Foundry	Google Vertex AI
Isolating unit	Foundry resource and projects	Agents
Entry point	Responses API	Model-agnostic Google’s ADK (Agent Development Kit) for all communication
Per-agent identity	Entra Agent ID	Agent Identity

What is the difference between building and running agents in Azure AI Foundry vs. Google Vertex AI?

Both platforms support two paths for building and running agents: one that lets you build configuration-only agents without code, and the other is where you bring the code, the agent loop, choose the runtime, and more. Here’s a side-by-side comparison considering agents on the platforms.

Aspect	Azure AI Foundry	Google Vertex AI
How is an agent defined?	You can create prompt agents that are no-code configuration-only and also hosted agents where you bring and build your own code	You can have the no-code option with Google Agent Studio. You can have the hosted agent-equivalent with Google’s Agent Development Kit
Where does the agent run?	Prompt agents run on Foundry’s fully managed runtime, while hosted agents run in micro VMs on per-session sandboxes	Both types of agents run on Google’s managed runtime, Agent Runtime (formerly Agent Engine)
How do you develop an agent?	Prompt agents can be built from the Foundry portal directly or the Foundry SDK. Hosted agents can use an agent framework of their choice, such as LangGraph, OpenAI Agents SDK, Microsoft Agent Framework, among others	You can use the open-source and model-agnostic ADK by Google or use any other open-source framework of your choice and deploy it to Agent Runtime
What tools can the agent access?	Foundry agents have access to built-in tools like file search, code interpretation, etc. You can add any new tool using a remote MCP server. Foundry also has its RAG solution in Foundry IQ	Google Vertex AI agents use built-in tools, such as Grounding with Google Search, Vertex AI Search, RAG Engine, and code execution. In addition, you can also register your own APIs and MCP servers that you can route through Agent Gateway
What models can the agent use?	Azure AI Foundry gives you access to most frontier models, along with thousands of models via Hugging Face Hub	Google Vertex AI gives you access to most frontier models, including Gemini and Gemma, along with any models published in the Model Garden
What about multi-agent?	Currently, Foundry agents can only use A2A for calling remote agents. Incoming A2A calls that expose a Foundry agent as an A2A endpoint are currently in public preview as of August 2026	Google created the A2A protocol. The ADK or any other framework built for multi-agent orchestration and using A2A can be used with Google Vertex AI agents

Building the agent is only the beginning. How efficiently the agent works is primarily a function of the knowledge and context it has access to at any given time, especially while it is in action. Here’s a look at the different ways both platforms make knowledge and context available to their agents.

How do Azure AI Foundry and Google Vertex AI connect agents to the knowledge and context they need?

Agents in Azure AI Foundry can be registered in the Entra Agent Registry. Those agents have access to Azure services like Purview, Azure Database for PostgreSQL, Cosmos DB, as well as OneLake and SharePoint via Foundry IQ.

Similarly, Vertex AI agents have access to Google BigQuery, Colab Enterprise, and Google Search, among other things.

But there are many other ways agents can get knowledge and context from within and outside your enterprise systems. Here are the broad categories and how the two platforms support them.

Context	Azure AI Foundry	Google Vertex AI
Documents, files, APIs, and databases from the enterprise	Context grounding using Bing Search, Foundry IQ (SharePoint, OneLake, and Blob Storage), and Fabric; agents can use 1,400+ connectors to connect to enterprise systems	Context grounding using Google Search, along with access to Cloud Storage and Google Drive, BigQuery, and the Apigee API Hub
RAG context sources like vector and hybrid indexes	Foundry IQ knowledge bases built on Azure AI Search at the heart of agentic retrieval following the RAG pattern	Vertex AI Search, RAG Engine, and Vector Search powering the RAG pattern for Vertex AI agents
Knowledge graph, semantic layer, ontology, glossary, and lineage	No built-in graph or semantic layer or ontology within Azure AI Foundry currently. Fabric IQ has an ontology feature in preview that can be exposed to agents as a Foundry IQ knowledge source; otherwise, the best route is to go to an external tool via MCP	Similar to Azure AI Foundry, Google Vertex AI also doesn’t have a built-in graph, semantic layer, or ontology. The only option is to go to an external tool via MCP
Agent memory and session state	Azure AI Foundry supports short-term, long-term, and procedural memory, all of which is fully managed	Google Vertex AI separates session memory from cross-session memory using Sessions and Memory Bank, both of which are useful for agents

Notice that there are two clear patterns where neither platform makes it easy to bring context to the agent:

While you can connect a lot of enterprise systems to your agent, it involves several separate integrations. These are one-to-one integrations.
There’s no knowledge graph or semantic layer or ontology that stitches all the context and knowledge from various sources in your organization into one cogent and coherent context layer.

This is why agents often don’t get the right context, and often guess. What agents really need is a context layer spanning the entire enterprise.

Both agent platforms, Azure AI Foundry and Vertex AI, support this pattern. That’s where Atlan, the Context Layer for AI, comes into the picture. Here’s how Atlan gives agents on either platform the context they need.

How does Atlan help with context in Azure AI Foundry and Google Vertex AI?

Atlan is the Context Layer for AI. It serves agents on both platforms, Azure AI Foundry and Google Vertex AI, with the precise context they need at any given time. Atlan’s MCP server delivers context to agents at runtime by bringing context from all enterprise systems into the enterprise context layer. Atlan accumulates, organizes, and curates context using three core capabilities.

Enterprise Data Graph is a graph that contains the entity relationships between systems, assets, code, products, and more. Active Ontology distills organizational language and knowledge into glossary terms, domains, metrics, and relationships as a semantic layer, aligned with Open Semantic Interchange. And the Context Engineering Studio lets you build Context Repos for your projects, run evaluations across different contexts, and deploy the version that works best for your agents.

Atlan’s enterprise context layer offloads the complexity of making context available to agents. It’s platform-agnostic and speaks MCP, which is why Foundry agents and Vertex agents can both talk to it and reach the same context at any given point.

Moving forward with agentic platforms for Azure and Google Cloud

AI Foundry and Vertex AI are both flagship agentic platforms for Azure and Google Cloud. They reach the same end goal, giving you the capability to create and customize agents, but each implements it slightly differently.

Both platforms support most major frontier models, let you use MCP for tool calls, and allow multi-agent orchestration using A2A.

Whichever platform you choose, the key constraint for agents is their ability to reach the right context at the right time during a run. In large enterprises, organizational knowledge, process documentation, communication channels, and decision registers, in other words, context, is spread across hundreds of systems.

Both platforms contain information within their own boundaries. For agents to act effectively, they need context from multiple systems at once, which is where the need for a context layer comes in. Atlan is the Context Layer for AI: rather than maintaining separate integrations for each platform in your enterprise, you point your platform to Atlan instead.

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FAQs about Azure AI Foundry vs. Google Vertex AI

1. Is Azure AI Foundry the same as Microsoft Foundry?

Yes, Azure AI Foundry was rebranded as Microsoft Foundry in November 2025. Microsoft Foundry lets you build agents, access models, and extend agents using enterprise-grade tools and knowledge with a single management plane, governance, security, and observability.

2. Is Google Vertex AI the same as the Gemini Enterprise Agent Platform?

Yes. Google Vertex AI evolved into the Gemini Enterprise Agent Platform at Google Cloud Next 2026. The core functionality of Vertex AI is now part of the Gemini Enterprise Agent Platform, which lets you build and reuse AI agents, choose a runtime, and apply enterprise governance throughout. It is a distinct product from Gemini Enterprise, the business-user-facing app that Google Agentspace became in October 2025.

3. Do both Azure AI Foundry and Google Vertex AI support MCP?

Yes, both platforms support MCP servers.

4. Which agent frameworks can you use for creating agents on Azure AI Foundry and Google Vertex AI?

Azure AI Foundry allows hosted agents with Microsoft Agent Framework, LangGraph, or the OpenAI Agents SDK, among many others. Prompt agents don’t need any framework at all. Google Vertex AI uses the open-source Google ADK project, but you can use any open-source agent framework and deploy agents using Agent Runtime.

5. How can agents on Azure AI Foundry and Google Vertex AI use the Atlan MCP server?

Both platforms can use Atlan’s MCP server to get enterprise context to agents. The mechanism differs slightly, but the end goal is the same: a Foundry agent adds Atlan’s MCP server as a remote MCP tool, and a Vertex AI agent built with the ADK has built-in constructs to connect to Atlan’s MCP server as a client.

Sources

What is an Agent Context Layer? Architecture Guide
Agent Context Layer Design: Principles and Decisions
Context Repository for AI Agents: A Practical Guide for 2026
Context Engineering for Multi-Agent Systems: A 2026 Guide
What Snowflake Cortex Needs from a Context Layer in 2026
Genie Ontology and the Atlan Context Layer: Better Together
Context Engineering 101: How to Build a Deep Context Layer for AI Analysts
What is MCP (Model Context Protocol): A 2026 Guide
What OpenAI’s Data Agent Reveals About Enterprise AI
Gartner on Context Graphs: Top Insights, Capabilities & Implementation Recommendations for 2026
Context Engineering for AI Governance: Complete 2026 Guide
Semantic Layers: The Complete Guide for 2026
Who Should Own the Context Layer: Data Teams vs. AI Teams? | A 2026 Guide
Context Layer vs. Semantic Layer: What’s the Difference & Which Layer Do You Need for AI Success?
What is Context Engineering: Why Does Enterprise AI Depend On It in 2026?
Context Graph vs Knowledge Graph: Key Differences for AI
Context Graph: Definition, Architecture, and Implementation Guide
Context Graph vs Ontology: Key Differences for AI
What Is Ontology in AI? Key Components and Applications
Context Layer 101: Why It’s Crucial for AI
Combining Knowledge Graphs With LLMs: Complete Guide
What Is an AI Analyst? Definition, Architecture, Use Cases, ROI
Ontology vs Semantic Layer: Understanding the Difference for AI-Ready Data
What Is Conversational Analytics for Business Intelligence?
Google Agentspace (Now Gemini Enterprise): What It Is, How It Works

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Atlan is the Context Layer for AI. It translates business knowledge, including data definitions, working procedures, and governance policies, into context AI can actually use. This knowledge lives in a single Enterprise Data Graph that every team and AI agent can reach.

In Atlan's AI Labs benchmark, adding this context improved AI's text-to-SQL accuracy by 38%.

Atlan is recognized as a Leader across multiple Gartner reports and Forrester Waves, and is trusted by over 400 enterprises representing $10T+ in market cap, including Mastercard, Workday, General Motors, CME Group, HubSpot, FOX, Virgin Media O2, and Elastic.

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