H2O.ai vs SAP Predictive Analytics comparison

H2O.ai and SAP are both solutions in the Data Science Platforms category. H2O.ai is ranked #15 with an average rating of 7.7, while SAP is ranked #27. H2O.ai holds a 1.9% mindshare in Data Science Platforms, compared to SAP’s 0.9% mindshare. Additionally, 100% of H2O.ai users are willing to recommend the solution, compared to 100% of SAP users who would recommend it.

H2O.ai

Read 10 H2O.ai reviews

1,331 Views
1,238 Comparison Views

100% willing to recommend

SAP Predictive Analytics

Read 3 SAP Predictive Analytics reviews

426 Views
422 Comparison Views

100% willing to recommend

H2O.ai

SAP Predictive Analytics

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Executive SummaryUpdated on Dec 5, 2024

SAP Predictive Analytics and H2O.ai compete in predictive analytics, each with distinct strengths. H2O.ai seems to have an edge with advanced machine learning frameworks and flexibility, providing better value in feature-rich environments.

Features: SAP Predictive Analytics integrates well within the SAP ecosystem, facilitating workflow simplification and automated predictive model creation for SAP users. H2O.ai offers open-source machine learning, scalability with big data, and integration support with R and Python, allowing for customizable machine learning operations.

Ease of Deployment and Customer Service:SAP Predictive Analytics provides cohesive integration within its suite, making deployment smoother for existing SAP users. H2O.ai offers flexible deployment options on various platforms with strong community and support, yielding more adaptability in diverse environments compared to SAP's integrated approach.

Pricing and ROI:SAP Predictive Analytics involves higher setup costs but offers high ROI for SAP-invested businesses by reducing operational disruptions. H2O.ai has a lower entry cost, appealing to organizations with established analytical infrastructures, achieving strong ROI through scalability and a comprehensive feature set, often proving more cost-efficient with better long-term returns.

To learn more, read our detailed H2O.ai vs. SAP Predictive Analytics Report (Updated: December 2025).

Buyer's Guide

H2O.ai vs. SAP Predictive Analytics

December 2025

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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

15th

Average Rating

7.6

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

Model Monitoring (4th)

SAP Predictive Analytics

Ranking in Data Science Platforms

27th

Average Rating

8.6

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of January 2026, in the Data Science Platforms category, the mindshare of H2O.ai is 1.9%, up from 1.5% compared to the previous year. The mindshare of SAP Predictive Analytics is 0.9%, up from 0.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms Market Share Distribution
Product	Market Share (%)
H2O.ai	1.9%
SAP Predictive Analytics	0.9%
Other	97.2%

Data Science Platforms

Featured Reviews

Muhammad Adnan

Senior Manager - AI at Shamal Holding

Have improved machine learning model automation and reduced decision-making time

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. H2O.ai could benefit from enhanced integration with real-time versus offline data sources, as well as improvements in productionalization solutions, including better deployment options on platforms like Azure and CI/CD integration. One of the features I'd like to see included in upcoming releases of H2O.ai pertains to the growing trend of Generative AI, with applications for LLM-based models and vector databases. I would like to see a solution similar to Azure AI Foundry, which provides the flexibility to integrate different LLMs into applications, including H2O-GPT and other models for varied applications.

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Abdul-Rasool

Senior Practice Manager - Head of SAP at a tech consulting company with 201-500 employees

Easy to implement, good data forecasting and reporting

This solution works for acquired data but not live, real-time data. If we connect it to a live backend system then we cannot perform predictive analytics on top of that. We have to first upload data to the cloud, manage the staging environment, and then perform the analysis. In the next release of this solution, I would like to see more automation in generating the models. The system can suggest the dimensions and measures that should be used, and pre-populate some of the information based on that. Power users would not have as much need for this, but this type of automation would be very helpful for business end-users.

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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."

"It is helpful, intuitive, and easy to use. The learning curve is not too steep."

"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 features are the machine learning tools, the support for Jupyter Notebooks, and the collaboration that allows you to share it across people."

"The ease of use in connecting to our cluster machines."

"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 feature of H2O.ai is that it is plug-and-play."

"AutoML helps in hands-free initial evaluations of efficiency/accuracy of ML algorithms."

More H2O.ai pros

"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

"The model management features could be improved."

"H2O.ai can improve in areas like multimodal support and prompt engineering."

"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."

"It needs a drag and drop GUI like KNIME, for easy access to and visibility of workflows."

"Referring to bullet-3 as well, H2O DataFrame manipulation capabilities are too primitive."

"It lacks the data manipulation capabilities of R and Pandas DataFrames. We would kill for dplyr offloading H2O."

More H2O.ai cons

"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

14%

Computer Software Company

11%

Educational Organization

Manufacturing Company

No data available

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	2
Midsize Enterprise	3
Large Enterprise	7

No data available

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...

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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...

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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...

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Comparisons

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Compared 28% of the time

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Compared 13% of the time

Amazon SageMaker vs H2O.ai

Compared 10% of the time

KNIME Business Hub vs H2O.ai

Compared 8% of the time

dotData vs H2O.ai

Compared 3% of the time

More H2O.ai Competitors

KNIME Business Hub vs SAP Predictive Analytics

Compared 33% of the time

IBM SPSS Statistics vs SAP Predictive Analytics

Compared 25% of the time

Saturn Cloud vs SAP Predictive Analytics

Compared 20% of the time

More SAP Predictive Analytics Competitors

Product Reports

Buyer's Guide

Data Science Platforms

January 2026

Download H2O.ai product report

Buyer's Guide

Data Science Platforms

January 2026

Download SAP Predictive Analytics product report

Also Known As

No data available

SAP BusinessObjects Predictive Analytics, BusinessObjects Predictive Analytics, BOPA

Overview

H2O is a fully open source, distributed in-memory machine learning platform with linear scalability. H2O’s supports the most widely used statistical & machine learning algorithms including gradient boosted machines, generalized linear models, deep learning and more. H2O also has an industry leading AutoML functionality that automatically runs through all the algorithms and their hyperparameters to produce a leaderboard of the best models. The H2O platform is used by over 14,000 organizations globally and is extremely popular in both the R & Python communities.

H2O.ai

SAP® Predictive Analytics software brings predictive insight to business users, analysts, data scientists, and developers in your company. Unlock the potential of Big Data from virtually any source with the power of predictive automation. By automating the building and management of sophisticated predictive models to deliver insight in real time, this software makes it easier to make better, more profitable decisions across the enterprise.

SAP

Sample Customers

poder.io, Stanley Black & Decker, G5, PWC, Comcast, Cisco

mBank

Buyer's Guide

H2O.ai vs. SAP Predictive Analytics

December 2025

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