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Altair RapidMiner vs SAS Predictive Analytics comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Jun 8, 2025

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 Predictive Analytics
2nd
Average Rating
8.6
Reviews Sentiment
7.0
Number of Reviews
24
Ranking in other categories
Data Science Platforms (7th)
SAS Predictive Analytics
Ranking in Predictive Analytics
9th
Average Rating
7.0
Reviews Sentiment
7.6
Number of Reviews
2
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of June 2025, in the Predictive Analytics category, the mindshare of Altair RapidMiner is 14.9%, down from 18.8% compared to the previous year. The mindshare of SAS Predictive Analytics is 3.3%, up from 3.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Predictive Analytics
 

Featured Reviews

Rathnam Makam - PeerSpot reviewer
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.
it_user1139529 - PeerSpot reviewer
Drag-and-drop functionality makes the interface easy to use, but the technical support needs to be improved
There are not many people deploying models using this solution, which is a problem. I have done some cross-development and have found that when I am building models with the open-source software, the accuracy is better. For categorical data, the models built by SAS Emailer are very complex compared to those built by the open-source version. Technical support could be improved because they take too long to answer our queries. Models that are created are a block box, and you can't see the details.

Quotes from Members

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

Pros

"The GUI capabilities of the solution are excellent. Their Auto ML model provides for even non-coder data scientists to deploy a model."
"The solution is very intuitive and powerful."
"Using the GUI, I can have models and algorithms drag and drop nodes."
"The most valuable feature of RapidMiner is that it can read a large number of file formats including CSV, Excel, and in particular, SPSS."
"The solution is stable."
"The best part of RapidMiner is efficiency."
"The most valuable features are the Binary classification and Auto Model."
"RapidMiner is very easy to use."
"The most valuable features are forecasting and reporting."
"The most valuable feature is its flexibility and the ability to integrate with SAS."
 

Cons

"I would appreciate improvements in automation and customization options to further streamline processes."
"The server product has been getting updated and continues to be better each release. When I started using RapidMiner, it was solid but not easy to set up and upgrade."
"If they could include video tutorials, people would find that quite helpful."
"RapidMiner would be improved with the inclusion of more machine learning algorithms for generating time-series forecasting models."
"Improve the online data services."
"The price of this solution should be improved."
"It would be helpful to have some tutorials on communicating with Python."
"I think that they should make deep learning models easier."
"Technical support could be improved because they take too long to answer our queries."
"I think that this solution should be more compatible with other software, including open-source solutions."
 

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."
"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."
"I used an educational license for this solution, which is available free of charge."
"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."
"If SAS were more flexible in terms of licensing then that would be good, because it costs more than other solutions."
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Top Industries

By visitors reading reviews
University
11%
Computer Software Company
11%
Educational Organization
10%
Financial Services Firm
9%
No data available
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
 

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...
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.
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...
Ask a question
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Overview

Information not available
 

Sample Customers

PayPal, Deloitte, eBay, Cisco, Miele, Volkswagen
MetLife
Find out what your peers are saying about Altair RapidMiner vs. SAS Predictive Analytics and other solutions. Updated: June 2025.
859,129 professionals have used our research since 2012.