Altair RapidMiner vs Domino Data Science Platform comparison

Altair and Domino Data Lab are both solutions in the Data Science Platforms category. Altair is ranked #7 with an average rating of 8.1, while Domino Data Lab is ranked #17 with an average rating of 8.0. Altair holds a 7.0% mindshare in Data Science Platforms, compared to Domino Data Lab’s 2.6% mindshare. Additionally, 96% of Altair users are willing to recommend the solution, compared to 100% of Domino Data Lab users who would recommend it.

Altair RapidMiner

Read 24 Altair RapidMiner reviews

4,556 Views
3,964 Comparison Views

96% willing to recommend

Domino Data Science Platform

Read 2 Domino Data Science Platform reviews

1,360 Views
1,333 Comparison Views

100% willing to recommend

Altair RapidMiner

Domino Data Science Platform

Comparison Buyer's Guide

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Executive SummaryUpdated on Sep 14, 2025

Altair RapidMiner and Domino Data Science Platform are competing in the data science software arena. Domino Data Science Platform seems to have the upper hand due to its comprehensive features, making it well-suited for enterprise-level applications.

Features: Altair RapidMiner offers comprehensive data processing, analytics capabilities, and automation alongside model deployment. Domino Data Science Platform features strong collaboration and reproducibility, with seamless integrations with various tools and technologies, providing a versatile environment for scalable enterprise projects.

Ease of Deployment and Customer Service: Altair RapidMiner supports straightforward deployment with minimal setup and reliable customer service. Domino Data Science Platform, while involving a more complex deployment process, offers extensive customer service options, beneficial for complex projects.

Pricing and ROI: Altair RapidMiner is cost-effective, appealing for strict budgets with favorable ROI. Domino Data Science Platform is initially more expensive but offers higher ROI through its feature-rich, scalable environment, ideal for large enterprises pursuing growth and innovation.

To learn more, read our detailed Altair RapidMiner vs. Domino Data Science Platform Report (Updated: September 2025).

Buyer's Guide

Altair RapidMiner vs. Domino Data Science Platform

September 2025

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Helped 869,566 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 (3rd)

Domino Data Science Platform

Ranking in Data Science Platforms

17th

Average Rating

7.6

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of October 2025, in the Data Science Platforms category, the mindshare of Altair RapidMiner is 7.0%, down from 7.5% compared to the previous year. The mindshare of Domino Data Science Platform is 2.6%, down from 2.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms Market Share Distribution
Product	Market Share (%)
Altair RapidMiner	7.0%
Domino Data Science Platform	2.6%
Other	90.4%

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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AHMEDSAHRAOUI

Machine Learning Engineer at Unemployed

Accelerated machine learning model development with seamless deployment

We used Domino Data Science Platform for developing and working with machine learning models. It facilitated end-to-end development processes. Domino is based on Git, enabling collaboration similar to using Git. Each user operates on their own equivalent of a branch or fork, and once finished, they…

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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."

"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"

"Scalability is not really a concern with RapidMiner. It scales very well and can be used in global implementations."

"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 best part of RapidMiner is efficiency."

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

"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."

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

More Altair RapidMiner pros

"The workspaces, which are like wrappers of Docker containers, made it easy to start development environments using Domino."

"The scalability of the solution is good; I'd rate it four out of five."

Cons

"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."

"Improve the online data services."

"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."

"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."

"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."

"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."

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

"The price of this solution should be improved."

More Altair RapidMiner cons

"The predictive analysis feature needs improvement."

"The deployment of large language models (LLMs) could be improved."

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."

"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."

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

Information not available

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Top Industries

By visitors reading reviews

University

11%

Computer Software Company

10%

Manufacturing Company

10%

Educational Organization

10%

Financial Services Firm

40%

Manufacturing Company

Insurance Company

Healthcare Company

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...

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 Domino Data Science Platform?

The deployment of large language models (LLMs) could be improved. Currently, Domino provides a simple server that cannot handle big deployments, which is not suitable for LLMs.

See all answers

What is your primary use case for Domino Data Science Platform?

See all answers

What advice do you have for others considering Domino Data Science Platform?

It's important to have a DevOps team well-versed with cloud-native solutions to manage Domino effectively. Relying solely on data scientists might not be sufficient. I'd rate the solution eight out...

See all answers

Comparisons

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Compared 14% of the time

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Compared 8% of the time

Alteryx vs Altair RapidMiner

Compared 6% of the time

Microsoft Azure Machine Learning Studio vs Altair RapidMiner

Compared 4% of the time

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Databricks vs Domino Data Science Platform

Compared 26% of the time

Dataiku vs Domino Data Science Platform

Compared 24% of the time

Amazon SageMaker vs Domino Data Science Platform

Compared 19% of the time

Alteryx vs Domino Data Science Platform

Compared 8% of the time

KNIME Business Hub vs Domino Data Science Platform

Compared 7% of the time

More Domino Data Science Platform Competitors

Product Reports

Buyer's Guide

Altair RapidMiner

October 2025

Download Altair RapidMiner product report

Buyer's Guide

Data Science Platforms

September 2025

Download Domino Data Science Platform product report

Also Known As

No data available

Domino Data Lab Platform

Interactive Demo

Demo not available

Altair

Domino Data Lab

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

Domino provides a central system of record that keeps track of all data science activity across an organization. Domino helps data scientists seamlessly orchestrate AWS hardware and software toolkits, increase flexibility and innovation, and maintain required IT controls and standards. Organizations can automatically keep track of all data, tools, experiments, results, discussion, and models, as well as dramatically scale data science investments and impact decision-making across divisions. The platform helps organizations work faster, deploy results sooner, scale rapidly, and reduce regulatory and operational risk.

Domino Data Lab

Sample Customers

PayPal, Deloitte, eBay, Cisco, Miele, Volkswagen

Allstate, GSK, AstraZeneca, Federal Reserve, US Navy, Bristol Myers Squibb, Bayer, BNP Paribas, Moodys, New York Life

Buyer's Guide

Altair RapidMiner vs. Domino Data Science Platform

September 2025

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