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H2O.ai vs Microsoft Azure Machine Learning Studio comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 5, 2024

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

H2O.ai
Ranking in Data Science Platforms
18th
Average Rating
7.6
Reviews Sentiment
6.8
Number of Reviews
10
Ranking in other categories
Model Monitoring (4th)
Microsoft Azure Machine Lea...
Ranking in Data Science Platforms
4th
Average Rating
7.8
Reviews Sentiment
7.1
Number of Reviews
62
Ranking in other categories
AI Development Platforms (5th)
 

Mindshare comparison

As of October 2025, in the Data Science Platforms category, the mindshare of H2O.ai is 1.7%, up from 1.5% compared to the previous year. The mindshare of Microsoft Azure Machine Learning Studio is 4.9%, down from 6.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Science Platforms Market Share Distribution
ProductMarket Share (%)
Microsoft Azure Machine Learning Studio4.9%
H2O.ai1.7%
Other93.4%
Data Science Platforms
 

Featured Reviews

Abhay Vyas - PeerSpot reviewer
Advanced model selection and time efficiency meet needs but documentation and fusion model support are needed
Even though H2O.ai provides the best model, there could be improvements in certain areas. For instance, when you want to work with fusion models, H2O.ai doesn't provide that kind of information. Currently, it provides individual models as outcomes. If it could offer combinations of models, such as suggesting using XGBoost along with SVM for wonderful results, that fusion model concept would be a good option for developers. I hope the fusion model concept will be implemented soon in H2O.ai. Regarding documentation, I faced challenges as I didn't see much information from a documentation perspective. When I was trying to learn how to train and test H2O.ai, there was limited documentation available. If they could improve in that area, it would be really beneficial.
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 ease of use in connecting to our cluster machines."
"AutoML helps in hands-free initial evaluations of efficiency/accuracy of ML algorithms."
"The most valuable features are the machine learning tools, the support for Jupyter Notebooks, and the collaboration that allows you to share it across people."
"One of the most interesting features of the product is their driverless component. The driverless component allows you to test several different algorithms along with navigating you through choosing the best algorithm."
"I have utilized the AutoML feature in H2O.ai, which is one of the very powerful features where you don't need to worry about which algorithm is best for your model."
"It is helpful, intuitive, and easy to use. The learning curve is not too steep."
"H2O.ai provides better flexibility where I could examine more models and obtain results, and based on these results, I could make the next set of decisions."
"Fast training, memory-efficient DataFrame manipulation, well-documented, easy-to-use algorithms, ability to integrate with enterprise Java apps (through POJO/MOJO) are the main reasons why we switched from Spark to H2O."
"The integration with Azure services enhances workflow and meets my expectations."
"The ability to do the templating and be able to transfer it so that I can easily do multiple types of models and data mining is a valuable aspect of this solution. You only have to set up the flows, the templates, and the data once and then you can make modifications and test different segmentations throughout."
"The platform as a service provides user-friendly instruments, making the experience easy."
"The product's initial setup phase is easy."
"The most valuable feature of this solution is the ability to use all of the cognitive services, prebuilt from Azure."
"The notebook feature allows you to write inquiries and create dashboards. These dashboards can integrate with multiple databases, such as Excel, HANA, or SQL Server."
"The solution is integrated with our Microsoft Azure tenant, and we don't have to go anywhere else outside the tenant."
"The drag-and-drop interface is good."
 

Cons

"On the topic of model training and model governance, this solution cannot handle ten or twelve models running at the same time."
"It lacks the data manipulation capabilities of R and Pandas DataFrames. We would kill for dplyr offloading H2O."
"H2O.ai can improve in areas like multimodal support and prompt engineering."
"It needs a drag and drop GUI like KNIME, for easy access to and visibility of workflows."
"I would like to see more features related to deployment."
"The interpretability module has room for improvement. Also, it needs to improve its ability to integrate with other systems, like SageMaker, and the overall integration capability."
"One improvement I would like to see in H2O.ai is regarding the integration capabilities with different data sources, as I've seen platforms like DataIQ and DataBricks offer great integration with various data sources."
"Referring to bullet-3 as well, H2O DataFrame manipulation capabilities are too primitive."
"The regulatory requirements of the product need improvement."
"The initial setup of Microsoft Azure Machine Learning Studio was rigorous for someone new like me, but mastering it made things simpler."
"The pricing policy should be improved."
"The solution must increase the amount of data sources that can be integrated."
"The data preparation capabilities need to be improved."
"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."
"I cannot comment on specific improvements yet as we are still exploring and need more time to identify the areas that require enhancements."
"Stability-wise, you may face certain problems when you fail to refresh the data in the solution."
 

Pricing and Cost Advice

"We have seen significant ROI where we were able to use the product in certain key projects and could automate a lot of processes. We were even able to reduce staff."
"I used the free student license for a few months to operate the solution, but I'll have to pay for it if I want to do more now."
"I rate the solution's pricing a four on a scale of one to ten, where one is cheap, and ten is expensive."
"On a scale from one to ten, with ten being overpriced, I would rate the price of this solution at six."
"It is less expensive than one of its competitors."
"The product's pricing is reasonable."
"ML Studio's pricing becomes a numbers game."
"There isn’t any such expensive costs and only a standard license is required."
"The platform's price is low."
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Top Industries

By visitors reading reviews
Financial Services Firm
16%
Computer Software Company
14%
Manufacturing Company
8%
Educational Organization
7%
Financial Services Firm
11%
Computer Software Company
9%
Manufacturing Company
9%
Performing Arts
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business2
Midsize Enterprise3
Large Enterprise7
By reviewers
Company SizeCount
Small Business23
Midsize Enterprise6
Large Enterprise30
 

Questions from the Community

What needs improvement with H2O.ai?
Even though H2O.ai provides the best model, there could be improvements in certain areas. For instance, when you want to work with fusion models, H2O.ai doesn't provide that kind of information. Cu...
What is your primary use case for H2O.ai?
I used H2O.ai on several POCs for my previous company, and it helped me find the best model. I needed to determine which model was performing better for job portal data. At that time, H2O.ai was ev...
What advice do you have for others considering H2O.ai?
For larger datasets, model computation or model training and testing typically takes considerable time because with individual models, you need to train and test each one. With H2O.ai, these concer...
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

poder.io, Stanley Black & Decker, G5, PWC, Comcast, Cisco
Walgreens Boots Alliance, Schneider Electric, BP
Find out what your peers are saying about H2O.ai vs. Microsoft Azure Machine Learning Studio and other solutions. Updated: September 2025.
872,778 professionals have used our research since 2012.