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H2O.ai vs SAP Predictive Analytics 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
20th
Average Rating
7.6
Reviews Sentiment
7.2
Number of Reviews
9
Ranking in other categories
Model Monitoring (5th)
SAP Predictive Analytics
Ranking in Data Science Platforms
27th
Average Rating
8.6
Reviews Sentiment
7.1
Number of Reviews
3
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of August 2025, in the Data Science Platforms category, the mindshare of H2O.ai is 1.8%, up from 1.4% compared to the previous year. The mindshare of SAP Predictive Analytics is 0.5%, up from 0.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Science Platforms
 

Featured Reviews

Kashif Yaseen - PeerSpot reviewer
Plug-and-play convenience enhances productivity but needs better multimodal support
We mostly used the solution in the domain that I'm working. We had most of the use cases around chatbots and conversational BI The solution was plug-and-play, meaning most of the components were handled by the solution itself rather than building them from scratch. This was useful for our banking…
Gary Cook - PeerSpot reviewer
Enables us to forecast and pull trends and has an easy installation
My rating for SAP Predictive Analytics would be an eight out of ten. If I have to be bold, I'll probably say that we're building away hours, and we are actually putting a lot of the actual predicting stuff back into the warehouse. So running it very bi-directionally. So I'm not sure what its integration features are at the moment, but that's an area we're going to look into in the next month or so.

Quotes from Members

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

Pros

"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."
"The ease of use in connecting to our cluster machines."
"The most valuable feature of H2O.ai is that it is plug-and-play."
"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."
"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."
"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."
"AutoML helps in hands-free initial evaluations of efficiency/accuracy of ML algorithms."
"It is helpful, intuitive, and easy to use. The learning curve is not too steep."
"I think the features of the actual ability to forecast and pull trends and correlations has been really good."
"The most valuable features are the analytics and reporting."
 

Cons

"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 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."
"It needs a drag and drop GUI like KNIME, for easy access to and visibility of workflows."
"H2O.ai can improve in areas like multimodal support and prompt engineering."
"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."
"The model management features could be improved."
"This solution works for acquired data but not live, real-time data."
 

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."
"The pricing is reasonable"
"A free trial version is available for testing out this solution."
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Top Industries

By visitors reading reviews
Financial Services Firm
16%
Computer Software Company
16%
Manufacturing Company
9%
Educational Organization
7%
No data available
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
 

Questions from the Community

What needs improvement with H2O.ai?
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 v...
What is your primary use case for H2O.ai?
Normally, I use H2O.ai for my machine learning tasks, and to give an example, some of the models that I've created using H2O.ai are taxi demand forecasting and a scoring model for leads. Most of my...
What advice do you have for others considering H2O.ai?
I would rate the technical support a nine. For organizations considering H2O.ai, my recommendations include appreciating it as a great and flexible tool for machine learning tasks without incurring...
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Also Known As

No data available
SAP BusinessObjects Predictive Analytics, BusinessObjects Predictive Analytics, BOPA
 

Overview

 

Sample Customers

poder.io, Stanley Black & Decker, G5, PWC, Comcast, Cisco
mBank
Find out what your peers are saying about H2O.ai vs. SAP Predictive Analytics and other solutions. Updated: July 2025.
864,155 professionals have used our research since 2012.