Altair RapidMiner vs SAP Predictive Analytics [EOL] comparison

Altair and SAP are both solutions in the Data Science Platforms category. Additionally, 96% of Altair users are willing to recommend the solution, compared to 100% of SAP users who would recommend it.

Altair RapidMiner

Read 26 Altair RapidMiner reviews

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4,708 Comparison Views

96% willing to recommend

SAP Predictive Analytics [EOL]

Read 3 SAP Predictive Analytics [EOL] reviews

100% willing to recommend

Altair RapidMiner

SAP Predictive Analytics [EOL]

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Executive SummaryUpdated on Jun 3, 2026

Altair RapidMiner and SAP Predictive Analytics [EOL] are products in the predictive analytics category. Altair RapidMiner's competitive pricing and strong support give it an advantage for budget-conscious buyers, while SAP Predictive Analytics [EOL]'s advanced features may justify its higher cost for those needing robust analytics solutions.

Features: Altair RapidMiner offers easy integration, extensive third-party APIs, and strong data visualization tools. SAP Predictive Analytics [EOL] provides sophisticated automated machine learning and seamless integration with SAP platforms. Altair RapidMiner is appealing to smaller businesses for its flexibility, whereas SAP's deep features benefit enterprises.

Ease of Deployment and Customer Service: Altair RapidMiner's cloud-based deployment allows for faster implementation with minimal IT resources. SAP Predictive Analytics [EOL] requires more intensive setup due to complex functionalities. Altair RapidMiner generally provides quicker, more responsive support compared to SAP.

Pricing and ROI: Altair RapidMiner offers a lower entry cost and a shorter path to ROI, making it valuable for budget-efficient businesses. SAP Predictive Analytics [EOL], though more expensive, provides significant long-term ROI through its comprehensive analytics and integration capabilities, favoring businesses with high analysis demands.

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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

Average Rating

8.4

Reviews Sentiment

6.9

Number of Reviews

Ranking in other categories

Predictive Analytics (6th), Data Science Platforms (11th)

SAP Predictive Analytics [EOL]

Average Rating

8.6

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

No ranking in other categories

Featured Reviews

Satesh P

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.

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Gary Cook

Executive at Empowered Analytics

Enables us to forecast and pull trends and has an easy installation

My rating for SAP Predictive Analytics would be an eight out of ten. If I have to be bold, I'll probably say that we're building away hours, and we are actually putting a lot of the actual predicting stuff back into the warehouse. So running it very bi-directionally. So I'm not sure what its integration features are at the moment, but that's an area we're going to look into in the next month or so.

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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 auto modeling has reduced end-of-line defect rates by approximately 18% in the first year after deploying the predictive quality models, translating directly into reduced scrap, lower rework costs, and better throughput."

"The solution is very intuitive and powerful."

"Since the beginning, we had a really nice onboarding experience with RapidMiner."

"The best part of RapidMiner is efficiency."

"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."

"Using the GUI, I can have models and algorithms drag and drop nodes."

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

"I've been using a lot of components from the Strategic Extension and Python Extension."

More Altair RapidMiner pros

"The most valuable features are the analytics and reporting."

"SAP Predictive Analytics is better suited for business users because it hides the complexity of the model, whereas Microsoft Azure Machine Learning provides a lot more flexibility for technical professionals to tweak the model."

"We always purchase SAP support because it is very good."

"I think the features of the actual ability to forecast and pull trends and correlations has been really good."

"I have found that the solution is very stable."

Cons

"Improve the online data services."

"Altair RapidMiner can be improved as the cloud-native story is still maturing."

"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."

"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."

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

"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."

"In the Mexican or Latin American market, it's kind of pricey."

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

More Altair RapidMiner cons

"The license fee appears to be prohibitively expensive and overly secretive, leading our clients to opt for cloud-based solutions that only charge for data storage and processing time."

"This solution works for acquired data but not live, real-time data."

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."

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

"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."

"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."

"A free trial version is available for testing out this solution."

"The pricing is reasonable"

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Top Industries

By visitors reading reviews

Manufacturing Company

12%

Financial Services Firm

10%

University

Educational Organization

Outsourcing Company

17%

Construction Company

17%

Comms Service Provider

Hospitality Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	12
Midsize Enterprise	5
Large Enterprise	10

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...

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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...

See all answers

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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Compared 5% of the time

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Product Reports

Buyer's Guide

Altair RapidMiner

July 2026

Download Altair RapidMiner product report

Buyer's Guide

Data Science Platforms

June 2026

Download SAP Predictive Analytics [EOL] product report

Also Known As

No data available

SAP BusinessObjects Predictive Analytics, BusinessObjects Predictive Analytics, BOPA

Overview

Altair RapidMiner is a GUI-driven, code-free data science tool ideal for users seeking efficiency and user-friendliness, featuring automated data cleaning and versatile model support for diverse tasks.

Altair RapidMiner offers an accessible platform with drag-and-drop functionality, supporting multiple file formats to streamline data science workflows. It enables quick prototyping and integrates with APIs, Python, and R, enhancing user flexibility. Comprehensive documentation and tutorials support learning, while features like model fine-tuning and predictive analytics cater to advanced analysis. Enhancements in automation and deep learning, alongside improvements in data service integration and metadata handling, remain a focus for development.

What are the key features of Altair RapidMiner?

Code-Free Interface: Simplifies data science with GUI support.
Drag-and-Drop Functionality: Streamlines process setup.
Automated Data Cleaning: Saves time in data preparation.
Machine Learning Algorithms: Supports a wide range for various tasks.
API & Python/R Integration: Enhances flexibility for data projects.
Tutorials & Documentation: Provides robust learning resources.

What benefits and ROI can users expect?

Efficiency: Reduces time spent on data processing.
Flexibility: Offers adaptable integration options.
Scalability: Suited for both educational and business environments.
Usability: User-friendly interface encourages wider adoption.
Predictive Capabilities: Empowers advanced business analytics.

Industries such as telecom and finance utilize Altair RapidMiner for tasks like data preparation and forecasting. Universities employ it for education and research projects, while businesses apply it to areas such as financial crime management and market analysis. It assists companies in predicting customer behavior and analyzing pharmaceutical data, allowing seamless integration with other systems.

Altair

SAP Predictive Analytics [EOL] offered a powerful platform for creating predictive models that supported business decision-making by utilizing historical data to anticipate future trends.

SAP Predictive Analytics [EOL] was designed to integrate with existing SAP environments, allowing businesses to leverage their existing data infrastructure. It provided users with intuitive tools to automate data preparation and model management, simplifying complex analytical processes. Data scientists could efficiently build and deploy predictive models to address specific business questions. SAP emphasized ease of deployment and scalability, ensuring the platform met the needs of data-driven enterprises.

What are the key features?

Automated Model Creation: Streamlines the process of generating predictive models, reducing time and effort.
Data Preparation: Simplifies data cleaning and preparation, ensuring high-quality input for analysis.
Seamless Integration: Easily integrates with SAP environments, enhancing data accessibility.

What benefits should users consider in reviews?

Informed Decision-Making: Provides insights that help businesses make data-informed decisions.
Efficiency in Analysis: Reduces the time needed to develop and deploy predictive models.
Scalability: Supports expanding data needs as businesses grow.

In industries like manufacturing and retail, SAP Predictive Analytics [EOL] helped optimize supply chains and inventory management by forecasting demand trends. Financial sector users implemented it to enhance risk analysis and fraud detection models, providing valuable insights for mitigating potential risks.

SAP

Sample Customers

PayPal, Deloitte, eBay, Cisco, Miele, Volkswagen

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