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 9 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: July 2025).

Buyer's Guide

Altair RapidMiner vs. H2O.ai

July 2025

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Helped 864,053 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

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

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

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

"The solution is very intuitive and powerful."

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

"I like not having to write all solutions from code. Being able to drag and drop controls, enables me to focus on building the best model, without needing to search for syntax errors or extra libraries."

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

"One of the most valuable features is the built-in data tuning feature. Once the model is built, we often struggle to increase its accuracy, but RapidMiner allows us to fine-tune variables. For Example, when working on a project, we can adjust the number of nodes or the depth of trees to see how accuracy changes. This flexibility lets us achieve higher accuracy compared to traditional automated machine-learning models"

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."

"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."

"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."

"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."

"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."

Cons

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

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

"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."

"I would like to see all users have access to all of the deep learning models, and that they can be used easily."

"RapidMiner isn't cheap. It's a complete solution, but it's costly."

"Improve the online data services."

"Altair RapidMiner needs updates to its examples, particularly in business and marketing areas, and to the tool itself. The user interface should be improved."

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

More Altair RapidMiner cons

"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."

"The model management features could be improved."

"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."

"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."

More H2O.ai cons

Pricing and Cost Advice

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

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

"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."

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

"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."

"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%

Educational Organization

10%

Manufacturing Company

Financial Services Firm

16%

Computer Software Company

16%

Manufacturing Company

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...

See all answers

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

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Compared 51% of the time

Dataiku vs Altair RapidMiner

Compared 16% of the time

Tableau vs Altair RapidMiner

Compared 7% of the time

Alteryx vs Altair RapidMiner

Compared 7% of the time

Altair Knowledge Studio vs Altair RapidMiner

Compared 1% of the time

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Compared 25% of the time

Databricks vs H2O.ai

Compared 19% of the time

Amazon SageMaker vs H2O.ai

Compared 19% of the time

Microsoft Azure Machine Learning Studio vs H2O.ai

Compared 16% of the time

SAP Predictive Analytics vs H2O.ai

Compared 4% of the time

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Product Reports

Buyer's Guide

Altair RapidMiner

July 2025

Download Altair RapidMiner product report

Buyer's Guide

Data Science Platforms

July 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

July 2025

Free Report: Altair RapidMiner vs. H2O.ai

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