Altair RapidMiner vs H2O.ai comparison

Altair and H2O.ai are both solutions in the Data Science Platforms category. Altair is ranked #7 with an average rating of 8.1, while H2O.ai is ranked #20 with an average rating of 7.0. Altair holds a 7.7% mindshare in Data Science Platforms, compared to H2O.ai’s 1.8% mindshare. Additionally, 96% of Altair users are willing to recommend the solution, compared to 100% of H2O.ai users who would recommend it.

Altair RapidMiner

Read 24 Altair RapidMiner reviews

5,302 Views
4,560 Comparison Views

96% willing to recommend

H2O.ai

Read 8 H2O.ai reviews

1,198 Views
1,138 Comparison Views

100% willing to recommend

Altair RapidMiner

H2O.ai

Comparison Buyer's Guide

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Executive SummaryUpdated on Jun 8, 2025

Altair RapidMiner and H2O.ai are competing in the data analytics and machine learning sector. H2O.ai has the upper hand due to its advanced machine learning capabilities and innovative features, providing superior value despite higher costs.

Features: Altair RapidMiner offers a user-friendly interface with extensive data preparation tools, effective visualization, and automation capabilities. It facilitates a no-code approach, making it accessible for non-programmers. H2O.ai stands out with its advanced machine learning algorithms and integration with popular data science languages. It provides flexible deployment options and deep integration with APIs and cloud computing environments.

Room for Improvement: Altair RapidMiner could enhance its generative AI adaptation and extend its current feature set to better handle complex datasets. H2O.ai might improve its deployment complexity and provide more intuitive onboarding processes. Both products could benefit from broader algorithmic explanations to avoid bias in model selection and deployment.

Ease of Deployment and Customer Service: Altair RapidMiner is known for its straightforward deployment process and responsive customer support. H2O.ai, while offering more complex deployment, provides a wealth of scalability options and technical expertise within its support services, ensuring robust assistance for complex technical issues.

Pricing and ROI: Altair RapidMiner is perceived to have a balanced pricing structure with good initial ROI. Although H2O.ai is more costly upfront, its advanced analytics capabilities justify the expense, often leading to higher ROI in intricate use cases.

To learn more, read our detailed Altair RapidMiner vs. H2O.ai Report (Updated: June 2025).

Buyer's Guide

Altair RapidMiner vs. H2O.ai

June 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

Altair RapidMiner

Ranking in Data Science Platforms

7th

Average Rating

8.6

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

Predictive Analytics (2nd)

H2O.ai

Ranking in Data Science Platforms

20th

Average Rating

7.6

Reviews Sentiment

7.2

Number of Reviews

Ranking in other categories

Model Monitoring (5th)

Mindshare comparison

As of July 2025, in the Data Science Platforms category, the mindshare of Altair RapidMiner is 7.7%, up from 7.1% compared to the previous year. The mindshare of H2O.ai is 1.8%, up from 1.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms

Featured Reviews

Rathnam Makam

Senior Technical Manager at a computer software company with 1,001-5,000 employees

A no-code tool that helps to build machine learning models

One challenge I encountered while implementing RapidMiner was the lack of documentation. Since there aren't as many users, finding resources to learn the tool was initially difficult. To overcome this hurdle, I believe RapidMiner could improve by providing more tutorials tailored for new users. I haven't explored the tool's latest version, so I'm unaware of the current features. However, I think it would be beneficial if they could enhance capabilities related to deep neural networks, provide better support for generating UI, and allow for importing and utilizing large language models.

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Kashif Yaseen

Trainee Decision Scientist at a tech services company with 1,001-5,000 employees

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…

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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

"The solution is very intuitive and powerful."

"Scalability is not really a concern with RapidMiner. It scales very well and can be used in global implementations."

"RapidMiner for Windows is an excellent graphical tool for data science."

"Altair RapidMiner is easy to use and intuitive with no coding required, making it a low code tool."

"What I like about RapidMiner is its all-in-one nature, which allows me to prepare, extract, transform, and load data within the same tool."

"RapidMiner is very easy to use."

"The solution is stable."

"The documentation for this solution is very good, where each operator is explained with how to use it."

More Altair RapidMiner 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."

"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."

"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."

Cons

"It would be helpful to have some tutorials on communicating with Python."

"If they could include video tutorials, people would find that quite helpful."

"Improve the online data services."

"One challenge I encountered while implementing RapidMiner was the lack of documentation. Since there aren't as many users, finding resources to learn the tool was initially difficult. To overcome this hurdle, I believe RapidMiner could improve by providing more tutorials tailored for new users."

"I would appreciate improvements in automation and customization options to further streamline processes."

"RapidMiner can improve deep learning by enhancing the features."

"A great product but confusing in some way with regard to the user interface and integration with other tools."

"In terms of the UI and SaaS, the user interface with KNIME is more appealing than RapidMiner."

More Altair RapidMiner cons

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

"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."

"The model management features could be improved."

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

"I would like to see more features related to deployment."

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

Pricing and Cost Advice

"I'm not fully aware of RapidMiner's price because we had licenses provided, but from my analysis, it's moderately priced, not too high or too low. It's worth the investment."

"For the university, the cost of the solution is free for the students and teachers."

"The client only has to pay the licensing costs. There are not any maintenance or hidden costs in addition to the license."

"Although we don't pay licensing fees because it is being used within the university, my understanding is that the cost is between $5,000 and $10,000 USD per year."

"I used an educational license for this solution, which is available free of charge."

"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."

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Top Industries

By visitors reading reviews

Computer Software Company

11%

University

10%

Educational Organization

10%

Manufacturing Company

Financial Services Firm

17%

Computer Software Company

14%

Manufacturing Company

10%

Educational Organization

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

What do you like most about RapidMiner?

RapidMiner is a no-code machine learning tool. I can install it on my local machine and work with smaller datasets. It can also connect to databases, allowing me to build models directly on the dat...

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What is your experience regarding pricing and costs for RapidMiner?

I started with a trial version. We are likely to purchase a license, which may offer additional features.

See all answers

What needs improvement with RapidMiner?

Currently, I am unsure of all the AI features available in Altair RapidMiner, particularly advanced AI capabilities like neural networks and deep learning. It would be beneficial if the platform co...

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What needs improvement with H2O.ai?

H2O.ai can improve in areas like multimodal support and prompt engineering. They are already working on updates and changes. Although I haven't explored all the new products they've added to their ...

See all answers

What is your primary use case for H2O.ai?

We mostly used the solution in the domain that I'm working. We had most of the use cases around chatbots and conversational BI.

See all answers

What advice do you have for others considering H2O.ai?

It is important to address data privacy concerns and ensure you're choosing the right vendor that meets your use case demands. Also, you may leave my name, Kashif, but please keep the company name ...

See all answers

Comparisons

KNIME vs Altair RapidMiner

Compared 51% of the time

Dataiku vs Altair RapidMiner

Compared 16% of the time

Tableau vs Altair RapidMiner

Compared 8% of the time

Alteryx vs Altair RapidMiner

Compared 7% of the time

More Altair RapidMiner Competitors

Dataiku vs H2O.ai

Compared 25% of the time

Amazon SageMaker vs H2O.ai

Compared 19% of the time

Databricks vs H2O.ai

Compared 19% of the time

Microsoft Azure Machine Learning Studio vs H2O.ai

Compared 17% of the time

SAP Predictive Analytics vs H2O.ai

Compared 4% of the time

More H2O.ai Competitors

Product Reports

Buyer's Guide

Altair RapidMiner

July 2025

Download Altair RapidMiner product report

Buyer's Guide

Data Science Platforms

July 2025

Download H2O.ai product report

Overview

Altair RapidMiner is a leading platform for data science and machine learning, offering a user-friendly interface with powerful tools for predictive analytics. It supports integration with APIs, Python, and cloud services for streamlined workflow creation.

RapidMiner provides an efficient data science environment featuring drag-and-drop functionality, automation tools, and a wide array of algorithms, making it adaptable for novices and experts alike. Users benefit from easy data preparation and analysis alongside robust support from a vibrant community. Challenges include better onboarding and deep learning model accessibility, alongside calls for enhanced image processing and large language model integration.

What features make Altair RapidMiner stand out?

Data science tools: Facilitates machine learning and predictive analytics.
Code-optional GUI: Enables non-programmers to create workflows seamlessly.
Integration: Supports APIs, Python, and cloud services.
Drag-and-drop functionality: Simplifies workflow creation for users.
Algorithm variety: Offers diverse methods for data analysis.

What benefits and ROI should users consider when evaluating?

Efficiency: Saves time in data preparation and workflow automation.
Accessibility: Intuitive design aids beginners and experts.
Community: Access to extensions and collaborative resources.
Documentation: Comprehensive tutorials enhance learning.

Altair RapidMiner is extensively used in business and academia, facilitating tasks like predictive analytics, segmentation, and deployment. In education, it supports data science teaching and research, while in industries such as telecom, banking, and healthcare, it's used for data mining, decision trees, and market analysis.

Altair

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

Sample Customers

PayPal, Deloitte, eBay, Cisco, Miele, Volkswagen

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

Buyer's Guide

Altair RapidMiner vs. H2O.ai

June 2025

Free Report: Altair RapidMiner vs. H2O.ai

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