Altair RapidMiner vs H2O.ai comparison

Altair and H2O.ai are both solutions in the Data Science Platforms category. Altair is ranked #6 with an average rating of 8.3, while H2O.ai is ranked #20 with an average rating of 7.0. Altair holds a 7.8% mindshare in Data Science Platforms, compared to H2O.ai’s 1.5% mindshare. Additionally, 95% of Altair users are willing to recommend the solution, compared to 100% of H2O.ai users who would recommend it.

Altair RapidMiner

Read 22 Altair RapidMiner reviews

5,232 Views
4,119 Comparison Views

95% willing to recommend

H2O.ai

Read 8 H2O.ai reviews

1,252 Views
903 Comparison Views

100% willing to recommend

Altair RapidMiner

H2O.ai

Comparison Buyer's Guide

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Executive SummaryUpdated on Mar 4, 2025

Altair RapidMiner and H2O.ai are both key players in the data science and machine learning sector. While H2O.ai is favored for its extensive features, Altair RapidMiner has a competitive edge in pricing and customer support.

Features: Altair RapidMiner includes user-friendly workflow automation, a vast library of pre-built algorithms, and the ability to efficiently read various file formats like CSV and Excel. H2O.ai, however, stands out with its advanced AutoML capabilities, detailed model interpretability, and support for numerous AI models, making it ideal for complex technical projects.

Room for Improvement: Altair RapidMiner could benefit from expanding its advanced automated features and integrating more cutting-edge AI tools. Enhancing community support can also be beneficial. H2O.ai might improve by simplifying its deployment process, which can be technically demanding, and enhancing user training resources to facilitate smoother onboarding for non-experts.

Ease of Deployment and Customer Service: Altair RapidMiner offers a simple deployment process with strong customer service, making it appealing to businesses seeking an easy introduction to data science tools. H2O.ai provides robust cloud-based deployment but may require advanced technical knowledge to implement fully. It compensates with comprehensive documentation and a supportive community.

Pricing and ROI: Altair RapidMiner is noted for its cost-effective initial setup and is credited with delivering good ROI, particularly for small to medium-sized enterprises. H2O.ai, although requiring a greater initial investment, delivers substantial ROI with its powerful features, which enhance model deployment efficiency, making it a wise choice for organizations focused on state-of-the-art technology.

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

Buyer's Guide

Altair RapidMiner vs. H2O.ai

January 2025

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Helped 839,422 peers since 2012

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

6th

Average Rating

8.6

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

Predictive Analytics (3rd)

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 March 2025, in the Data Science Platforms category, the mindshare of Altair RapidMiner is 7.8%, up from 6.5% compared to the previous year. The mindshare of H2O.ai is 1.5%, down from 1.5% 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

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

"It is easy to use and has a huge community that I can rely on for help. Moreover, it is interactive."

"The most valuable feature of RapidMiner is that it can read a large number of file formats including CSV, Excel, and in particular, SPSS."

"RapidMiner is very easy to use."

"The data science, collaboration, and IDN are very, very strong."

"The most valuable feature of RapidMiner is that it is code free. It is similar to playing with Lego pieces and executing after you are finished to see the results. Additionally, it is easy to use and has interesting utilities when preparing the data. It has a utility to automatically launch a series of models and show the comparisons. When finished with the comparisons you can select the best one, and deploy it automatically."

"The best part of RapidMiner is efficiency."

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

More Altair RapidMiner pros

"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."

"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."

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

"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."

Cons

"I would like to see more integration capabilities."

"Many things in the interface look nice, but they aren't of much use to the operator. It already has lots of variables in there."

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

"RapidMiner would be improved with the inclusion of more machine learning algorithms for generating time-series forecasting models."

"The biggest problem, not from a platform process, but from an avoidance process, is when you work in a heavily regulated environment, like banking and finance. Whenever you make a decision or there is an output, you need to bill it as an avoidance to the investigator or to the bank audit team. If you made decisions within this machine learning model, you need to explain why you did so. It would better if you could explain your decision in terms of delivery. However, this is an issue with all ML platforms. Many companies are working heavily in this area to help figure out how to make it more explainable to the business team or the regulator."

"The visual interface could use something like the-drag-and-drop features which other products already support. Some additional features can make RapidMiner a better tool and maybe more competitive."

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

"About twenty-five percent of my problems involve image processing, and I found RapidMiner lacking in this domain. While we work on OCR and similar tasks, RapidMiner hasn't been as engaged in that field as other models. Some other models also support email processing, but RapidMiner doesn't offer this feature."

More Altair RapidMiner cons

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

"The model management features could be improved."

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

"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."

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

"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."

Pricing and Cost Advice

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

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

"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."

"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

University

11%

Computer Software Company

11%

Financial Services Firm

10%

Educational Organization

Financial Services Firm

22%

Computer Software Company

11%

Manufacturing Company

Energy/Utilities Company

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'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.

See all answers

What needs improvement with RapidMiner?

The product must provide data-cleaning features. I could not use RapidMiner for data cleaning in one of my projects and had to use Python instead.

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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 52% of the time

Dataiku vs Altair RapidMiner

Compared 17% of the time

Alteryx vs Altair RapidMiner

Compared 8% of the time

Tableau vs Altair RapidMiner

Compared 7% of the time

TIBCO Statistica vs Altair RapidMiner

Compared 1% of the time

More Altair RapidMiner Competitors

Databricks vs H2O.ai

Compared 21% of the time

Dataiku vs H2O.ai

Compared 19% of the time

Amazon SageMaker vs H2O.ai

Compared 17% of the time

Microsoft Azure Machine Learning Studio vs H2O.ai

Compared 14% of the time

IBM Watson Studio vs H2O.ai

Compared 5% of the time

More H2O.ai Competitors

Product Reports

Buyer's Guide

Altair RapidMiner

February 2025

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Buyer's Guide

Data Science Platforms

February 2025

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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

January 2025

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