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Altair RapidMiner vs Cloudera Data Science Workbench comparison

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Comparison Buyer's Guide

Executive SummaryUpdated on Jun 3, 2026

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
11th
Average Rating
8.4
Reviews Sentiment
6.9
Number of Reviews
26
Ranking in other categories
Predictive Analytics (6th)
Cloudera Data Science Workb...
Ranking in Data Science Platforms
24th
Average Rating
7.0
Reviews Sentiment
6.9
Number of Reviews
2
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of October 2026, in the Data Science Platforms category, the mindshare of Altair RapidMiner is 3.2%, down from 7.0% compared to the previous year. The mindshare of Cloudera Data Science Workbench is 1.6%, up from 1.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Science Platforms Mindshare Distribution
ProductMindshare (%)
Altair RapidMiner3.2%
Cloudera Data Science Workbench1.6%
Other95.2%
Data Science Platforms
 

Featured Reviews

SP
Solution Architect at Hitachi Digital Services
Visual workflows have empowered teams to build and deploy reliable predictive maintenance models
The best features Altair RapidMiner offers in my experience are the visual workflow designer in AI Studio, which is the foundation of everything. Building complete machine learning pipelines, data ingestion, transformation, feature engineering, model training, validation, and deployment in a drag-and-drop visual environment without extensive coding is what makes this accessible to organizations that cannot staff a team of Python developers for every analytics project. That capability opens the door.Auto Model is the feature I lean on most when doing rapid prototyping with clients. It evaluates multiple algorithms automatically, surfaces the best-performing model for the data, and explains why. That dramatically compresses the experimentation phase. What would take a data scientist days of manual testing, Auto Model does in an hour.
Ismail Peer - PeerSpot reviewer
Program Management Lead Advisor at Unionbank Philippines
Useful for data science modeling but improvement is needed in MLOps and pricing
If you don't configure CDSW well, then it might be not useful for you. Deploying the tool can vary in complexity, but most of the time, it's relatively simple and straightforward. Triggering a job from data to production is easy, as the platform automates the deployment process. However, ensuring optimal resource allocation is essential for smooth operations.

Quotes from Members

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

Pros

"Using the GUI, I can have models and algorithms drag and drop nodes."
"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."
"It is easy to use and has a huge community that I can rely on for help. Moreover, it is interactive."
"Altair RapidMiner is easy to use and intuitive with no coding required, making it a low code tool."
"The most valuable features are the Binary classification and Auto Model."
"The best part of RapidMiner is efficiency."
"RapidMiner has moved companies toward being more data-driven."
"Altair RapidMiner is appreciated for its ease of use and the CRISP data mining model it supports, covering steps like data preparation, data understanding, and business understanding."
"The Cloudera Data Science Workbench is customizable and easy to use."
"I appreciate CDSW's ability to logically segregate environments, such as data, DR, and production, ensuring they don't interfere with each other. The deployment of machine learning is fast and easy to manage. Its API calls are also fast."
 

Cons

"Altair RapidMiner needs updates to its examples, particularly in business and marketing areas, and to the tool itself. The user interface should be improved."
"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."
"It's supposedly very intuitive, and yet, I don't know where to click, honestly."
"In terms of the UI and SaaS, the user interface with KNIME is more appealing than RapidMiner."
"I would like to see all users have access to all of the deep learning models, and that they can be used easily."
"The product must provide data-cleaning features."
"I would like to see more integration capabilities."
"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."
"Running this solution requires a minimum of 12GB to 16GB of RAM."
"The tool's MLOps is not good. It's pricing also needs to improve."
"We found this solution a little bit difficult to scale."
 

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."
"The product is expensive."
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Top Industries

By visitors reading reviews
Manufacturing Company
13%
Financial Services Firm
10%
University
9%
Computer Software Company
8%
Financial Services Firm
27%
Outsourcing Company
12%
Healthcare Company
6%
Construction Company
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business12
Midsize Enterprise5
Large Enterprise10
No data available
 

Questions from the Community

What is your experience regarding pricing and costs for RapidMiner?
My experience with pricing, setup cost, and licensing shows that the licensing model is based on Altair Units, which is their shared token-based system across their product portfolio and is flexibl...
What needs improvement with RapidMiner?
Altair RapidMiner can be improved by enhancing the newer GenAI features, which are interesting but honestly still quite early, and the documentation does not yet match the ambition of what they are...
What is your primary use case for RapidMiner?
My main use case for Altair RapidMiner is predictive quality analysis on the manufacturing site, as Wagner Spraytech manufactures spray finishing equipment and we generate a significant amount of o...
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Also Known As

No data available
CDSW
 

Overview

 

Sample Customers

PayPal, Deloitte, eBay, Cisco, Miele, Volkswagen
IQVIA, Rush University Medical Center, Western Union
Find out what your peers are saying about Altair RapidMiner vs. Cloudera Data Science Workbench and other solutions. Updated: September 2026.
915,533 professionals have used our research since 2012.