Azure AI Foundry vs Google Vertex AI comparison

Microsoft and Google are both solutions in the AI Development Platforms category. Microsoft is ranked #7 with an average rating of 8.0, while Google is ranked #3 with an average rating of 8.1. Additionally, 100% of Microsoft users are willing to recommend the solution, compared to 100% of Google users who would recommend it.

Azure AI Foundry

Read 16 Azure AI Foundry reviews

495 Views
183 Comparison Views

100% willing to recommend

Google Vertex AI

Read 14 Google Vertex AI reviews

6,731 Views
5,991 Comparison Views

100% willing to recommend

Azure AI Foundry

Google Vertex AI

Comparison Buyer's Guide

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Executive Summary

We performed a comparison between Azure AI Foundry and Google Vertex AI based on real PeerSpot user reviews.

Find out in this report how the two AI Development Platforms solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

To learn more, read our detailed Azure AI Foundry vs. Google Vertex AI Report (Updated: December 2025).

Buyer's Guide

Azure AI Foundry vs. Google Vertex AI

December 2025

Download the complete report

Helped 881,082 peers since 2012

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Categories and Ranking

Azure AI Foundry

Ranking in AI Development Platforms

7th

Ranking in AI-Agent Builders

3rd

Average Rating

8.0

Reviews Sentiment

5.7

Number of Reviews

Ranking in other categories

Low-Code Development Platforms (10th), Integration Platform as a Service (iPaaS) (10th)

Google Vertex AI

Ranking in AI Development Platforms

3rd

Ranking in AI-Agent Builders

6th

Average Rating

8.2

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

No ranking in other categories

Featured Reviews

Sudhakar Pyndi

Data, Analytics & Ai Senior Director, Enterprise Architecture at a comms service provider with 10,001+ employees

Document processing has accelerated contract reviews and enabled rapid development of AI-driven supply chain solutions

With regard to security, compliance, or governance features in Azure AI Foundry, this is something that we have started looking into, primarily using Microsoft Purview for our governance, data governance. There is this new module called DSPM for AI, and we are exploring it while trying to operationalize it with different policies and so forth, but we're still not where we want to be on the governance, AI governance side. It's a process and a path, and we are trying to work through that right now. Azure AI Foundry can be improved from the governance perspective, as a lot can be done. The promising part is the recent announcement on the Foundry control plane. A couple of days back, there was an announcement regarding it bringing in some of the gaps that were on the platform, so it's a really positive direction in terms of where it's going. More governance is what is lacking, but the control plane will really play a big role there.

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Hamada Farag

Technology Consultant at Beta Information Technology

Customization and integration empower diverse AI applications

We are familiar with most Google Cloud services, particularly infrastructure services, storage, compute, AI tools, containerization, GCP containerization, and cloud SQL. We are familiar with approximately eighty percent of Google's services, primarily related to infrastructure, AI, containers, backup, storage, and compute. We are familiar with Gemini AI and Google Vertex AI, and we have completed some exercises and cases with our customers for Google AI. We use automation in machine learning. I work with a team where everyone has specific responsibilities. We have design and development processes in place. Based on my experience, I would rate Google Vertex AI a 9 out of 10.

Read full review

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"The features of Azure AI Foundry that I appreciate the most include the model catalog and the capability to deploy all of these different models, especially now that they've added Anthropic, which was the big one that was missing."

"Having Azure AI Foundry as a tool has benefited our organization significantly because our team has five data scientists, and this type of tool makes everything much faster."

"The benefit of using Azure AI Foundry for the organization is saving time, so saving time and then making money—now that we have this, many people were not doing this because it took them so long to do that research, that has been fixed."

"The features of Azure AI Foundry that I appreciate the most include the containment of all the agents and the ability to see all agents in a single dashboard and to have access to all of them from one portal."

"The most beneficial feature for enhancing our customer experience is that it is easy to use for them and for us to implement."

"Azure AI Foundry has helped me reduce the time taken for AI app and agent development significantly because it takes over a lot of the infrastructure work of connecting to these models."

"Azure AI Foundry has helped reduce the time taken for AI app and agent development cycles by approximately 50% for one use case."

"The most beneficial feature of Azure AI Foundry for enhancing customer experience is the ability to use Azure Functions to call things outside of Azure AI Foundry, making it more comprehensive as a feature."

More Azure AI Foundry pros

"Google Vertex AI is an out-of-the-box and very easy-to-use solution."

"The integration of AutoML features streamlines our machine-learning workflows."

"Vertex comes with inbuilt integration with GCP for data storage."

"Vertex AI possesses multiple libraries, so it eliminates the need for extensive coding."

"The best feature of Google Vertex AI is the ease of use, along with the integration with the rest of the Google ecosystem and the way models can be made available outside Google through endpoints."

"The most useful function of Google Vertex AI for me is the ease of integration, as we can easily create a prompt and integrate it into our current system."

"The monitoring feature is a true life-saver for data scientists. I give it a ten out of ten."

"It provides the most valuable external analytics."

More Google Vertex AI pros

Cons

"For Azure AI Foundry, there is no actual clear pricing structure, which can be confusing for customers to understand, as every feature that you activate has its own price, and it is not very clear sometimes to define the pricing."

"Azure AI Foundry can be improved by adding educational features."

"I would improve Azure AI Foundry by adding more functions within Foundry itself, as right now it is quite basic in what it does."

"One of the big things Azure AI Foundry could improve is continuously evolving the governance elements and how, while I know they exist, the more control we can have over different elements and observation of what different agents are doing, the better."

"My experience with deploying Azure AI Foundry is that, at this point in time, given the limited capabilities available in Foundry, we built pro-code agents, hosted them, containerized them, and deployed them."

"Azure AI Foundry can be improved from the governance perspective, as a lot can be done."

"In Azure AI Foundry, I did not see many tools inside to test it that can really help us. I wish there were more tooling."

"My biggest critique is some of the fragmentation of their different AI services they have, including AI Open, OpenAI, Azure OpenAI, and Azure Foundry. They feel very disjointed sometimes, so having a unified single experience for all of that would be ideal."

More Azure AI Foundry cons

"I'm not sure if I have suggestions for improvement."

"It is not completely mature and needs some features and functions. The interface needs to be more user-friendly."

"I believe that Vertex AI is a robust platform, but its effectiveness depends significantly on the domain knowledge of the developer using it. While Vertex AI does offer support through the console UI in the Google Cloud environment, it is better suited for technical members who have a deeper understanding of machine learning concepts. The platform may be challenging for business process developers (BPDUs) who lack extensive technical knowledge, as it involves intricate customization and handling numerous parameters. Effectively utilizing Vertex AI requires not only familiarity with machine learning frameworks like TensorFlow or PyTorch but also a proficiency in Python programming. The complexity of these requirements might pose challenges for less technically oriented users, making it crucial to have a solid foundation in both machine learning principles and Python coding to extract the full value from Vertex AI. It would be beneficial to have a streamlined process where we can leverage the capabilities of Vertex AI directly through the BigQuery UI. This could involve functionalities such as creating machine learning models within the BigQuery UI, providing a more user-friendly and integrated experience. This would allow users to access and analyze data from BigQuery while simultaneously utilizing Vertex AI to build machine learning models, fostering a more cohesive and efficient workflow."

"The solution is stable, but it is quite slow. Maybe my data is too large, but I think that Google could improve Vertex AI's training time."

"Google can improve Google Vertex AI in terms of analysis and accuracy. When passing a very large context, instead of receiving vague responses, it would be better if the system could prompt users not to pass overly large prompts and provide clearer guidance on how to fine-tune Gemini for specific use cases."

"We used AutoML feature for developing AI models automatically, but we are not comfortable with the performance of those models."

"I've noticed that using chat activity often presents a broader range of options and insights for a well-constructed question. Improving the knowledge base could be a key aspect for enhancement—expanding the information sources to enhance the generation process."

"It is not completely mature and needs some features and functions. The interface needs to be more user-friendly."

More Google Vertex AI cons

Pricing and Cost Advice

Information not available

"The price structure is very clear"

"The solution's pricing is moderate."

"The Versa AI offers attractive pricing. With this pricing structure, I can leverage various opportunities to bring value to my business. It's a positive aspect worth considering."

"I think almost every tool offers a decent discount. In terms of credits or other stuff, every cloud provider provides a good number of incentives to onboard new clients."

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Top Industries

By visitors reading reviews

Financial Services Firm

21%

Manufacturing Company

15%

Outsourcing Company

13%

Retailer

12%

Computer Software Company

13%

Financial Services Firm

Manufacturing Company

Educational Organization

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	2
Midsize Enterprise	2
Large Enterprise	13

By reviewers
Company Size	Count
Small Business	5
Midsize Enterprise	3
Large Enterprise	7

Questions from the Community

What is your experience regarding pricing and costs for Azure AI Foundry?

I would need to ask my technical team about my experience with the pricing, setup costs, and licensing.

See all answers

What needs improvement with Azure AI Foundry?

The platform's effect on my management of privacy, performance, and compliance across different regions is quite complex because Azure AI Foundry does not make it very clear how to deploy. We set u...

See all answers

What is your primary use case for Azure AI Foundry?

My main use cases for Azure AI Foundry include deploying AI applications to perform document comparison, translation services, and a chat feature, helping the digital AI team at our company. Curren...

See all answers

What is your experience regarding pricing and costs for Google Vertex AI?

I purchased Google Vertex AI directly from Google, as we are a partner of Google. I would rate the pricing for Google Vertex AI as low; the price is affordable.

See all answers

What needs improvement with Google Vertex AI?

We used AutoML feature for developing AI models automatically, but we are not comfortable with the performance of those models. We have to do some fine-tuning, hyperparameter optimization, and othe...

See all answers

What is your primary use case for Google Vertex AI?

We are developing AI models and agents using Google Vertex AI platform, and we are deploying them using Google Vertex AI platform on Google Cloud Platform, GCP. With just one single platform, Googl...

See all answers

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Compared 21% of the time

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Compared 14% of the time

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Compared 9% of the time

Amazon SageMaker vs Google Vertex AI

Compared 7% of the time

NVIDIA DGX Cloud vs Google Vertex AI

Compared 3% of the time

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Product Reports

Buyer's Guide

Azure AI Foundry

January 2026

Download Azure AI Foundry product report

Buyer's Guide

Google Vertex AI

January 2026

Download Google Vertex AI product report

Overview

Azure AI Foundry harnesses advanced AI technologies to streamline complex tasks across industries, offering cutting-edge solutions that enhance business processes and boost efficiency.

Azure AI Foundry integrates seamlessly into business environments, leveraging AI to transform traditional operations. It supports diverse applications by providing robust machine learning capabilities that drive innovation and enable intelligent automation. Designed to handle large-scale data analytics, it empowers users to make data-driven decisions swiftly and accurately, thereby optimizing resources and workflows.

What are the key features of Azure AI Foundry?

Scalable Machine Learning: Provides tools to build, deploy, and manage machine learning models at scale.
Data Integration: Offers seamless integration with data storage solutions for comprehensive data processing.
Security and Compliance: Ensures high-level security measures and compliance standards for data handling.
Interactive AI Development: Facilitates interactive and collaborative development environments for AI projects.

What benefits and ROI should users look for when evaluating Azure AI Foundry?

Cost Efficiency: Reduces operational costs through automation and optimized resource usage.
Improved Decision-Making: Enhances decision-making with real-time insights and analytics.
Innovation Acceleration: Supports rapid deployment of AI solutions, driving technological advancement.
Enhanced Productivity: Boosts productivity by automating routine tasks, allowing focus on strategic activities.

Azure AI Foundry is utilized across multiple industries, including finance, healthcare, and manufacturing, where it aids in predictive analytics, patient data management, and supply chain optimization. Its ability to integrate AI-driven insights into everyday operations helps these sectors enhance their efficiency and innovate steadily in dynamic markets.

Microsoft

Build, deploy, and scale ML models faster, with pre-trained and custom tooling within a unified artificial intelligence platform.

Google

Buyer's Guide

Azure AI Foundry vs. Google Vertex AI

December 2025

Free Report: Azure AI Foundry vs. Google Vertex AI

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