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Google Cloud AI Platform vs Microsoft Azure Machine Learning Studio comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Apr 20, 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 Cloud AI Platform
Ranking in AI Development Platforms
8th
Average Rating
7.8
Reviews Sentiment
7.1
Number of Reviews
9
Ranking in other categories
No ranking in other categories
Microsoft Azure Machine Lea...
Ranking in AI Development Platforms
3rd
Average Rating
7.6
Reviews Sentiment
7.0
Number of Reviews
61
Ranking in other categories
Data Science Platforms (4th)
 

Mindshare comparison

As of April 2025, in the AI Development Platforms category, the mindshare of Google Cloud AI Platform is 4.3%, down from 6.6% compared to the previous year. The mindshare of Microsoft Azure Machine Learning Studio is 7.3%, down from 13.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.
AI Development Platforms
 

Featured Reviews

Vipul-Kumar - PeerSpot reviewer
An AI platform AI Platform to train your machine learning models at scale, to host your trained model in the cloud, and to use your model to make predictions about new data
I think it's the it it also has has evolved quite a bit over the last few years, and Google Cloud folks have been getting, more and more services. But I think from a improvement standpoint, so maybe they can look at adding more algorithms, so adding more AI algorithms to their suite.
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 platform's Google Vision API is particularly valuable."
"Since the model could be trained in just a couple of hours and deploying it took only a few minutes, the entire process took less than an hour."
"On GCP, we are exposing our API services to our clients so that they send us their information. It can be single individual records or it can be a batch of their clients."
"The feedback left about these tools was really helpful and informative for us"
"Some of the valuable features are the vast amount of services that are available, such as load balancer, and the AI architecture."
"The solution is able to read 90% of the documents correctly with a 10% error rate."
"A range of a a wide range of algorithms, EIM voice mails, which can be plugged in right away into your solution into into into our solution, and then have platform that provides know, to to come up with an operational solution really quick."
"The initial setup is very straightforward."
"I like that it's totally easy to use. They have an AutoML solution, and their machine learning model is highly accurate. They also have a feature that can explain the machine learning model. This makes it easy for me to understand that model."
"I've developed a couple of chatbots using Azure OpenAI, leveraging its documented solutions and APIs. The tools available make it straightforward to implement machine learning solutions. However, there are challenges, such as hallucinations and security issues, but overall, it works well and is quite fast, allowing for the development of interesting projects."
"The most valuable feature is data normalization."
"The initial setup is very simple and straightforward."
"The most valuable feature is the knowledge bank, which allows us to ask questions and the AI will automatically pull the pre-prescribed responses."
"Their web interface is good."
"Their support is helpful."
"The product is well organized. The thing is how we will get the models to work within our code. We have some suggestions there, but we want to gain more experience and be ready to answer that because we are currently working on this and don't have all the answers yet. The tool is well organized. What I am very happy about is the ease of deploying new resources. You can easily create your pipeline within minutes."
 

Cons

"The initial setup was straightforward for me but could be difficult for others."
"Improvements in text extraction accuracy and pricing adjustments would be helpful."
"Customizations are very difficult, and they take time."
"It could be more clear, and sometimes there are errors that I don't quite understand."
"I think it's the it it also has has evolved quite a bit over the last few years, and Google Cloud folks have been getting, more and more services. But I think from a improvement standpoint, so maybe they can look at adding more algorithms, so adding more AI algorithms to their suite."
"At first, there were only the user-managed rules to identify the best attributes of the individual. Then, we came up with a truth set and developed different machine learning models with the help of that truth set, so now it's completely machine learning."
"One thing that I found is that Azure ML does not directly provide you with features on Google Cloud AI Platform, whereas Vertex provides some features of the platform."
"The model management on Google Cloud AI Platform could be better."
"In the Machine Learning Studio, particularly the Designer part, which is essentially Azure's demo designer, there is room for improvement. Many customers and users tend to switch to Microsoft Azure Multi-Joiners, which is a more basic version, but they do so internally. One area that could use enhancement is the process of connecting components. Currently, every time you want to connect a component, such as linking it to your storage or an instance like EC2, you have to input your username and password repeatedly. This can be quite cumbersome. Google, for instance, has made it more user-friendly by allowing easy access for connecting services within a workspace. In a workspace, you can set up various resources like storage, a database cluster, machine learning studio, and more. When connecting these services, there's no need to enter your username and password each time, making it a more efficient process. Another aspect to consider is the role of the designer, and they were to integrate a large language model to handle various tasks, it could significantly enhance the overall scalability and usability of the platform."
"Machine Learning Studio is more dependent on legacy Machine Learning algorithms. It would be beneficial for them to incorporate more services required for LLMs or LLM evaluation."
"The solution should be more customizable. There should be more algorithms."
"The regulatory requirements of the product need improvement."
"There's room for improvement in terms of binding the integration with Azure DevOps."
"I have found Databricks is a better solution because it has a lot of different cluster choices and better integration with MLflow, which is much easier to handle in a machine learning system."
"The product must improve its documentation."
"Enable creating ensemble models easier, adding more machine learning algorithms."
 

Pricing and Cost Advice

"The solution has an attractive starting program, which costs only 300 USD for a duration of three months. During this period, one can accomplish a lot of work on the solution."
"The pricing is on the expensive side."
"The price of the solution is competitive."
"For every thousand uses, it is about four and a half euros."
"The licenses are cheap."
"I am paying for it following a pay-as-you-go. So, the more I use it, the more it costs."
"On a scale from one to ten, with ten being overpriced, I would rate the price of this solution at six."
"To use MLS is fairly cheap. Even the paid account is something like $20/month, unless you are provisioning large numbers of VMs for a Hadoop cluster. The main MS makes money with this solution is forcing the user to deploy their model on REST API, and being charged each time the API is accessed. There are several pricing tiers for the API. If you do not use the API, then value of MLS is to create rapid experiments ($20/month). The resulting model is not exportable to use, thus you’ll have to recreate the algorithms in either R or Python, which is what I did. MLS results gave me a direction to work with, the actual work is mostly done in R and Python outside of MLS."
"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."
"In terms of pricing, for any cloud solution, you should know the tricks of the trade and how to use it, otherwise, you'll end up paying a lot of money irrespective of the cloud provider, so at least for Microsoft Azure Machine Learning Studio pricing versus AWS, I would rate it three out of five, with one being the most expensive, and five being the cheapest. It could be cheaper, but you also have to be careful when choosing the plans, for example, consider the architecture and a lot of other factors before choosing your plan, if you don't want to end up paying more. If your cloud provider has an optimizer that seems to be available in every provider, that would keep alerting you in terms of resources not being used as much, then that would help you with budgeting."
"Last year, we paid 60,000 for Microsoft Azure Machine Learning Studio in our department."
"It is less expensive than one of its competitors."
"I rate the solution's pricing a four on a scale of one to ten, where one is cheap, and ten is expensive."
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Top Industries

By visitors reading reviews
Computer Software Company
16%
Financial Services Firm
11%
Manufacturing Company
9%
University
8%
Financial Services Firm
13%
Computer Software Company
10%
Manufacturing Company
10%
Healthcare Company
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

What do you like most about Google Cloud AI Platform?
A range of a a wide range of algorithms, EIM voice mails, which can be plugged in right away into your solution into into into our solution, and then have platform that provides know, to to come up...
What is your experience regarding pricing and costs for Google Cloud AI Platform?
For the most part, the pricing is perfect sinceit grows with the use of my app. In some cases, they could be more specific about the pricing, especially for some AI features.
What is your primary use case for Google Cloud AI Platform?
I use Google Cloud AI Platform due to Firebase and the many APIs that are available with it.
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?
Pricing is considered to be top-segment and should be improved. I rate the pricing as three or four on a scale of one to ten in terms of affordability.
 

Also Known As

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

Overview

 

Sample Customers

Carousell
Walgreens Boots Alliance, Schneider Electric, BP
Find out what your peers are saying about Google Cloud AI Platform vs. Microsoft Azure Machine Learning Studio and other solutions. Updated: April 2025.
849,190 professionals have used our research since 2012.