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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."
"It is helpful, intuitive, and easy to use. The learning curve is not too steep."
"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."
"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."
"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."
"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 most valuable feature of H2O.ai is that it is plug-and-play."
"AutoML helps in hands-free initial evaluations of efficiency/accuracy of ML algorithms."
"Microsoft Azure Machine Learning Studio offers a hands-on experience with lots of flexibility."
"Their web interface is good."
"The integration with Azure services enhances workflow and meets my expectations."
"The interface is very intuitive."
"The drag-and-drop interface is good."
"The solution is very fast and simple for a data science solution."
"Split dataset, variety of algorithms, visualizing the data, and drag and drop capability are the features I appreciate most."
"The most valuable feature is its compatibility with Tensorflow."
 

Cons

"The model management features could be improved."
"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."
"I would like to see more features related to deployment."
"On the topic of model training and model governance, this solution cannot handle ten or twelve models running at the same time."
"H2O.ai can improve in areas like multimodal support and prompt engineering."
"It lacks the data manipulation capabilities of R and Pandas DataFrames. We would kill for dplyr offloading H2O."
"Referring to bullet-3 as well, H2O DataFrame manipulation capabilities are too primitive."
"It needs a drag and drop GUI like KNIME, for easy access to and visibility of workflows."
"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."
"If you want to be able to deploy your tools outside of Microsoft Azure, this is not the best choice."
"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 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."
"A problem that I encountered was that I had to pay for the model that I wanted to deploy and use on Azure Machine Learning, but there wasn't any option that that model can be used in the designer."
"While ML Studio does give you the ability to run a lot of transformations, it struggles when the transformations are a bit more complex, when your entire process is transformation-heavy."
"The platform's integration feature could be better."
 

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."
"There isn’t any such expensive costs and only a standard license is required."
"ML Studio's pricing becomes a numbers game."
"Last year, we paid 60,000 for Microsoft Azure Machine Learning Studio in our department."
"The solution operates on a pay-per-use model."
"There is a license required for this solution."
"On a scale from one to ten, with ten being overpriced, I would rate the price of this solution at six."
"There is a lack of certainty with the solution's pricing."
"I would rate the pricing an eight out of ten, with ten being very expensive. Not very expensive, not very cheap."
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Top Industries

By visitors reading reviews
Financial Services Firm
16%
Computer Software Company
15%
Manufacturing Company
8%
Educational Organization
7%
Financial Services Firm
12%
Manufacturing Company
10%
Computer Software 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.
869,832 professionals have used our research since 2012.