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Google Vertex AI vs Microsoft Azure Machine Learning Studio comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Jul 27, 2025

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

Google Vertex AI
Ranking in AI Development Platforms
2nd
Average Rating
8.4
Reviews Sentiment
6.7
Number of Reviews
12
Ranking in other categories
AI Infrastructure (1st)
Microsoft Azure Machine Lea...
Ranking in AI Development Platforms
4th
Average Rating
7.8
Reviews Sentiment
7.1
Number of Reviews
62
Ranking in other categories
Data Science Platforms (4th)
 

Mindshare comparison

As of September 2025, in the AI Development Platforms category, the mindshare of Google Vertex AI is 10.6%, down from 20.5% compared to the previous year. The mindshare of Microsoft Azure Machine Learning Studio is 4.8%, down from 10.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.
AI Development Platforms Market Share Distribution
ProductMarket Share (%)
Google Vertex AI10.6%
Microsoft Azure Machine Learning Studio4.8%
Other84.6%
AI Development Platforms
 

Featured Reviews

Hamada Farag - PeerSpot reviewer
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.
Takayuki Umehara - PeerSpot reviewer
Streamlined workflows with drag and drop convenience but needs enhancements in AI
I use Machine Learning Studio for system reselling and integration Machine Learning Studio is easy to use, with a significant feature being the drag and drop interface that enhances workflow without any complaints. It provides a return on investment and cost savings, proving beneficial for…

Quotes from Members

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

Pros

"The monitoring feature is a true life-saver for data scientists. I give it a ten out of ten."
"We extensively utilize Google Cloud's Vertex AI platform for our machine learning workflows. Specifically, we leverage the IO branch for EDA data in Suresh Live Virtual, employing Forte IT for training machine learning models. The AI model registry in Vertex AI is crucial for cataloging and managing various versions of the models we develop. When it comes to deploying models, we rely on Google Cloud's AI Prediction service, seamlessly integrating it into our workflow for real-time predictions or streaming. For monitoring and tracking the outcomes of model development, we employ Vertex AI Monitoring, ensuring a comprehensive understanding of the model's performance and results. This integrated approach within Vertex AI provides a unified platform for managing, deploying, and monitoring machine learning models efficiently."
"Google Vertex AI is better for deployment, configuration, delivery, licensing, and integration compared to other AI platforms."
"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."
"The most valuable feature we've found is the model garden, which allows us to deploy and use various models through the provided endpoints easily."
"Vertex AI possesses multiple libraries, so it eliminates the need for extensive coding."
"The drag-and-drop interface of Azure Machine Learning Studio has greatly improved my workflow."
"The solution is very fast and simple for a data science solution."
"The integration with Azure services enhances workflow and meets my expectations."
"The solution facilitates our production."
"Anyone who isn't a programmer his whole life can adopt it. All he needs is statistics and data analysis skills."
"The visualizations are great. It makes it very easy to understand which model is working and why."
"Azure Machine Learning Studio's most valuable features are the package from Azure AutoML. It is quite powerful compared to the building of ML in Databricks or other AutoMLs from other companies, such as Google and Amazon."
"Their web interface is good."
 

Cons

"It would be beneficial to have certain features included in the future, such as image generators and text-to-speech solutions."
"It is not completely mature and needs some features and functions. The interface needs to be more user-friendly."
"I think the technical documentation is not readily available in the tool."
"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."
"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."
"Google Vertex AI is good in machine learning and AI, but it lacks optimization."
"It takes a considerable amount of time to process, and I understand the technology behind why it takes this long, but this is something that could be reduced."
"The price could be improved."
"The interface is a bit overloaded."
"The speed of deployment should be faster, as should testing."
"Microsoft should also include more examples and tutorials for using this product.​"
"One problem I experience is that switching between multiple accounts can be difficult. I don't think there are any major issues. Mostly, the biggest challenge is to identify business solutions to this. The tool should keep on updating new algorithms and not stay static."
"Enable creating ensemble models easier, adding more machine learning algorithms."
"The high price of the product is an area of concern where improvements are required."
"The initial setup of Microsoft Azure Machine Learning Studio was rigorous for someone new like me, but mastering it made things simpler."
 

Pricing and Cost Advice

"The price structure is very clear"
"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."
"The solution's pricing is moderate."
"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."
"It is less expensive than one of its competitors."
"There isn’t any such expensive costs and only a standard license is required."
"I rate the solution's pricing a four on a scale of one to ten, where one is cheap, and ten is expensive."
"I would rate the pricing an eight out of ten, with ten being very expensive. Not very expensive, not very cheap."
"We pay only the Azure costs for what we use, which involves some subscription costs. But essentially, you pay for what you use. There are no extra costs in addition to the standard licensing fees."
"On a scale from one to ten, with ten being overpriced, I would rate the price of this solution at six."
"The platform's price is low."
"My team didn't deal with the licensing for Microsoft Azure Machine Learning Studio, so I'm unable to comment on pricing, but the money that was spent on the tool was worth it."
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Top Industries

By visitors reading reviews
Computer Software Company
14%
Financial Services Firm
11%
Manufacturing Company
9%
Educational Organization
6%
Financial Services Firm
12%
Computer Software Company
9%
Manufacturing Company
9%
University
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business4
Midsize Enterprise3
Large Enterprise6
By reviewers
Company SizeCount
Small Business23
Midsize Enterprise6
Large Enterprise30
 

Questions from the Community

What do you like most about Google Vertex AI?
We extensively utilize Google Cloud's Vertex AI platform for our machine learning workflows. Specifically, we leverage the IO branch for EDA data in Suresh Live Virtual, employing Forte IT for trai...
What is your experience regarding pricing and costs for Google Vertex AI?
They have different pricing models like pay-as-you-go or subscription model, and total cost of ownership. It is comparatively cheaper than Azure.
What needs improvement with Google Vertex AI?
Google Vertex AI is one of the best in the market, followed by Azure AI. It can be rated at eight or nine out of ten. It is not completely mature and needs some features and functions. The interfac...
Which do you prefer - Databricks or Azure Machine Learning Studio?
Databricks gives you the option of working with several different languages, such as SQL, R, Scala, Apache Spark, or Python. It offers many different cluster choices and excellent integration with ...
What do you like most about Microsoft Azure Machine Learning Studio?
The learning curve is very low. Operationalizing the model is also very easy within the Azure ecosystem.
What is your experience regarding pricing and costs for Microsoft Azure Machine Learning Studio?
The pricing for Microsoft Azure Machine Learning Studio is reasonable since it's pay as you go, meaning it won't cost excessively unless specific resources are used.
 

Also Known As

No data available
Azure Machine Learning, MS Azure Machine Learning Studio
 

Overview

 

Sample Customers

Information Not Available
Walgreens Boots Alliance, Schneider Electric, BP
Find out what your peers are saying about Google Vertex AI vs. Microsoft Azure Machine Learning Studio and other solutions. Updated: July 2025.
867,349 professionals have used our research since 2012.