Altair RapidMiner vs DataRobot comparison

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Altair RapidMiner

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Executive SummaryUpdated on Dec 21, 2025

Altair RapidMiner and DataRobot are prominent products in the data analytics and machine learning domain. DataRobot seems to have the upper hand due to its advanced automation capabilities, despite RapidMiner being commended for pricing and support.

Features: Altair RapidMiner excels in data preparation and visualization, offering a versatile interface for data tasks. It supports a drag-and-drop GUI, facilitates integration with various data sources, and enhances operator efficiency. DataRobot automates machine learning processes, combines model optimization with predictive analytics, and provides end-to-end integration, streamlining complex workflows.

Room for Improvement: Altair RapidMiner could benefit from improvements in generative AI adaptation, more robust integration capabilities, and expanded community support. DataRobot could enhance its user interface to be more beginner-friendly, broaden its feature set for non-automated tasks, and possibly reduce its high reliance on cloud services for flexibility in deployment.

Ease of Deployment and Customer Service: Altair RapidMiner supports flexible deployment with comprehensive support, aiding users throughout implementation. DataRobot simplifies deployment with cloud-based solutions, providing rapid access and operations. Altair's strong support contrasts with DataRobot’s efficient processes, critical in time-sensitive environments.

Pricing and ROI: Altair RapidMiner offers a lower cost option, valuable for teams focusing on data manipulation tasks, ensuring a satisfactory ROI through affordability. DataRobot requires a higher upfront investment but justifies it through advanced features and faster productivity gains. The decision between immediate savings with RapidMiner and the rich returns of DataRobot forms the basis of choice for tech buyers.

To learn more, read our detailed Altair RapidMiner vs. DataRobot Report (Updated: February 2026).

Altair RapidMiner vs. DataRobot

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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

Ranking in Predictive Analytics

4th

Average Rating

8.6

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

Data Science Platforms (8th)

DataRobot

Ranking in Predictive Analytics

5th

Average Rating

8.2

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

AI Development Platforms (15th), AIOps (15th), AI Observability (74th), AI Finance & Accounting (6th)

Mindshare comparison

As of February 2026, in the Predictive Analytics category, the mindshare of Altair RapidMiner is 6.6%, down from 16.5% compared to the previous year. The mindshare of DataRobot is 6.5%, down from 7.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Predictive Analytics Market Share Distribution
Product	Market Share (%)
Altair RapidMiner	6.6%
DataRobot	6.5%
Other	86.9%

Predictive Analytics

Featured Reviews

VictorSantos

Professor at Instituto Superior de Contabilidade e Administraçao de Coimbra

Utilize intuitive CRISP model support and predictive analytics features for effective data analysis

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 tool’s auto model feature is excellent as it allows simulation of models to select the best one. It is useful for predictive analytics with community support for model adjustments. I also find handling complex datasets promising, although there's a need for improvement with generative AI adaptation.

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Naqash Ahmed

Senior Data Reporting Analyst at University of Bradford

Automation has improved efficiency and decision-making while big data handling and transparency still need work

Aside from the many advantages of DataRobot, I believe there are areas that could be improved based on my experience. There is a lack of transparency in the models; sometimes it feels like a black box. For example, when I uploaded a large data set of about two gigabytes for processing, the time taken was slower than expected. Additionally, the handling of bigger data sets could be better, as it performs extremely well with smaller datasets but can lag with larger ones. The integration with some other tools used in our organization can also be challenging, and more flexibility for custom pre-processing and advanced model tuning would be beneficial. In terms of support and documentation, I believe improvements are needed. For instance, the response time from DataRobot could be quicker, which would be appreciated when we need assistance. The documentation is generally sufficient, but it can be lengthy and could use more real-world examples and step-by-step tutorials for better clarity. Lastly, creating a client community where users can share experiences and solutions might enhance the overall value and learning curve.

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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 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 solution is stable."

"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 data stored there. RapidMiner offers a wider range of operators than other tools like Dataiku, making it a better option for my needs."

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

"RapidMiner is very easy to use."

"We value the collaboration and governance features because it's a comprehensive platform that covers everything from data extraction to modeling operations in the ML language. RapidMiner is competitive in the ML space."

"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."

More Altair RapidMiner pros

"DataRobot is highly automated, allowing data scientists to build models easily."

"It's easy to do MLOps operations. It's a lot easier to manage jobs and see the logs if there's any drift in a model."

"We especially like the initial part of feature engineering, because feature engineering is included in most engines, but DataRobot has an excellent way of picking up the right features."

"Tasks such as model testing, feature engineering, and predictions that used to take us days or weeks can now be accomplished in hours."

"By automating highly technical aspects like model comparison, DataRobot enhances productivity and reduces project timelines from three months to less than one month."

"DataRobot can be easy to use."

Cons

"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."

"I would appreciate improvements in automation and customization options to further streamline processes."

"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."

"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."

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

"Currently, I am unsure of all the AI features available in Altair RapidMiner, particularly advanced AI capabilities like neural networks and deep learning."

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

"The price of this solution should be improved."

More Altair RapidMiner cons

"There are some performance issues."

"DataRobot is a UI-based tool, which means it cannot provide all the features I might manually implement through notebooks or Python. In this aspect, I see room for improvement in its functionality."

"There is a lack of transparency in the models; sometimes it feels like a black box."

"If we could include our existing Python or R code in DataRobot, we could make it even better. The DataRobot that we have is specific to an industry, but most of the time we would have our own algorithms, which are specific to our own use case. If we had a way by which we could integrate our proprietary things into DataRobot with a simple integration, it would help us a lot."

"The business departments will love to work with DataRobot because they use the tool to investigate their data, such as targeting what they want to investigate. They don't need any data scientists near them. They can investigate at eye level and bring into the BI tool, or can bring it to the data scientist. Data scientists can use this tool to bring increase the solution to the maximum. All the others can use it, but not to the maximum."

"Generative AI has taken pace, and I would like to see how DataRobot assists in doing generative AI and large language models."

Pricing and Cost Advice

"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."

"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."

"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 price of DataRobot is good because if you take the price of the solution which is approximately $65,000, it is less than a data scientist. There are very few data scientists available."

"We dropped the plan to use DataRobot, because we found the pricing to be on the higher sise. We liked DataRobot a lot, but due to the pricing, we dropped that idea."

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Top Industries

By visitors reading reviews

Manufacturing Company

12%

University

11%

Computer Software Company

10%

Financial Services Firm

13%

Manufacturing Company

13%

Computer Software Company

10%

Educational Organization

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	12
Midsize Enterprise	5
Large Enterprise	8

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...

What is your experience regarding pricing and costs for DataRobot?

While pricing falls more under my IT colleagues, from my perspective, the overall experience feels justified. The premium pricing is reasonable for the value provided, and I'd say it's worth the in...

What needs improvement with DataRobot?

What is your primary use case for DataRobot?

My main use case for DataRobot is to perform predictive analysis and automation of machine learning workflows. I use it to quickly build, test, and deploy models without extensive coding. One of th...

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Product Reports

Altair RapidMiner

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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

DataRobot captures the knowledge, experience and best practices of the world’s leading data scientists, delivering unmatched levels of automation and ease-of-use for machine learning initiatives. DataRobot enables users to build and deploy highly accurate machine learning models in a fraction of the time.

Sample Customers

PayPal, Deloitte, eBay, Cisco, Miele, Volkswagen

Harmoney, Zidisha, ONE Marketing, DonorBureau, Trupanion, Avant

Altair RapidMiner vs. DataRobot