Altair RapidMiner vs Alteryx vs DataRobot comparison

Read 87 Alteryx reviews

8,338 Views
1,417 Comparison Views

89% willing to recommend

Read 7 DataRobot reviews

3,239 Views
810 Comparison Views

100% willing to recommend

Altair RapidMiner

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Executive Summary

We performed a comparison between Altair RapidMiner, Alteryx, and DataRobot based on real PeerSpot user reviews.

Find out what your peers are saying about Alteryx, SAP, Anaplan and others in Predictive Analytics.

To learn more, read our detailed Predictive Analytics Report (Updated: May 2026).

Predictive Analytics

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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:

Mindshare comparison

As of May 2026, in the Predictive Analytics category, the mindshare of Altair RapidMiner is 6.1%, down from 15.2% compared to the previous year. The mindshare of Alteryx is 7.9%, down from 20.3% compared to the previous year. The mindshare of DataRobot is 5.9%, down from 9.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Predictive Analytics Mindshare Distribution
Product	Mindshare (%)
Alteryx	7.9%
Altair RapidMiner	6.1%
DataRobot	5.9%
Other	80.1%

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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ManojBehera

Senior Staff Cyber Security Cloud Data Architect at GE Healthcare

Automated complex ETL workflows have reduced coding effort and improved data integration

One area for improvement is in integrating mostly the data which comes from telemetry, where I can see some sort of improvisations can be made. It is a massive amount of unstructured data, and I believe Alteryx is able to handle it, but there can be some improvements. Suggestions for improvements in Alteryx include areas for increasing efficiency, particularly in processing telemetry data, which involves dealing with large volumes of unstructured data. Additionally, I believe when we use filter tools immediately after the input source, there can be slowdowns when handling massive data. The user experience of Alteryx is generally good, but there are areas for improvement from a user's perspective, particularly regarding user interface enhancements. I think there's always room for improvement, but otherwise, Alteryx has been a great tool for me. We haven't experienced significant disruptions while increasing data volumes, though I sense there could be performance issues as data grows exponentially. This is an area that could use improvement in Alteryx.

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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

"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."

"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"

"The solution is very intuitive and powerful."

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

"The tools have a complete function for doing data."

"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 best thing about RapidMiner is efficiency."

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

More Altair RapidMiner pros

"The value add of Alteryx is the agility for making changes, and speed of deployment."

"Technical support is very helpful."

"Overall, the solution as a whole is extremely useful."

"The tools are built-in. You just plug and play, drag and drop, once you understand how to use the tools, it is easy."

"I think the most valuable feature is the ease of use for my business scientists."

"We are taking the data from disparate systems and transforming and making the data available for further visualization."

"The main benefits that Alteryx provides for me are that it is easier to use, I do not have to code anything, so it is a non-coding tool, and it saves my time in that aspect."

"Although Alteryx is a paid software, it is equally able to provide better performance without writing a single line of code."

More Alteryx pros

"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."

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

"DataRobot can be easy to use."

"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."

"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."

"Previously we had five or six processes which used to be done manually by different people and that has been transformed using DataRobot because agents now are doing the same thing, resulting in a lot of money saved and around $2 million in cost savings for the bank."

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

"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."

More DataRobot pros

Cons

"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."

"It's supposedly very intuitive, and yet, I don't know where to click, honestly."

"The price of this solution should be improved."

"A great product but confusing in some way with regard to the user interface and integration with other tools."

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

"Altair RapidMiner needs updates to its examples, particularly in business and marketing areas, and to the tool itself. The user interface should be improved."

"On the other hand, compared to some other products, maybe the UI could be enhanced."

More Altair RapidMiner cons

"Configuration is very low and is not good for the customer end."

"All of the reports are migrated or exported in an Excel file, and most of the time, a business intelligence tool is required."

"The product's pricing needs improvement."

"When the workflows are huge and complex, it may take some time to refresh it."

"There are no ready models to use in analytics."

"It should have Linux support. It currently supports only Windows. It does not support any other platform. Its price should also be lower. Their US partner management program is actually unresponsive. One of the reasons why we don't have a formalized partnership plan with them is because their partner management team is atrociously unresponsive. This is something that they need to change."

"Alteryx's development environment could be improved as it requires installation locally and can't be developed in the cloud."

"Technical support is okay and could be better. Sometimes, it takes about two to five days to hear an answer from the technical support team."

More Alteryx cons

"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."

"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."

"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 are some performance issues."

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

"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."

"DataRobot can actually be improved by having access to multiple data repositories. It is lacking in the ways in which it ingests data, in which it transforms the data because we need a separate data manipulation tool for which we need to have somebody else."

More DataRobot cons

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."

"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."

"I don't know much about the licensing, but there are some additional costs for certain features."

"The license is really expensive, we cannot afford to have two or three. It takes away all the budget of my area."

"The license price of the solution is expensive."

"The price for Alteryx Designer is reasonable but the price for Alteryx Server for universal collaborations is too expensive."

"It can be a bit pricey, especially after the first year."

"I rate the tool's pricing a two out of ten."

"The desktop platform costs $5,000 per year. It's very costly."

"In my opinion, it's actually quite expensive."

More Alteryx pricing and cost advice

"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%

Financial Services Firm

11%

University

Educational Organization

Financial Services Firm

20%

Manufacturing Company

Computer Software Company

Retailer

Financial Services Firm

15%

Manufacturing Company

13%

Educational Organization

Construction 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

By reviewers
Company Size	Count
Small Business	33
Midsize Enterprise	16
Large Enterprise	57

By reviewers
Company Size	Count
Small Business	2
Midsize Enterprise	1
Large Enterprise	6

Questions from the Community

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 l...

What is your primary use case for RapidMiner?

I am using Altair RapidMiner ( /products/altair-rapidminer-reviews ) to analyze stock data and supply chain assessmen...

What is the Biggest Difference Between Alteryx and IBM SPSS Modeler?

One of the differences is that with Alteryx you can use it as an ETL and analytics tool. Please connect with me direc...

What is the Biggest Difference Between Alteryx and IBM SPSS Modeler?

Alteryx is an extremely easy and flexible data tool, flexible in terms of drag and drop toolset and also has python, ...

What is the Biggest Difference Between Alteryx and IBM SPSS Modeler?

I am not familiar with IBM SPSS Modeler, therefore, I cannot compare these two products. Regarding Alteryx I can say...

What is your experience regarding pricing and costs for DataRobot?

My experience with pricing, setup cost, and licensing reveals that the price points can be improved and DataRobot is ...

What needs improvement with DataRobot?

DataRobot can actually be improved by having access to multiple data repositories. It is lacking in the ways in which...

What is your primary use case for DataRobot?

My main use case for DataRobot is to give an agentic AI flavor to my different customers because many of my customers...

KNIME Business Hub vs Altair RapidMiner

Comparisons

Compared 13% of the time

Dataiku vs Altair RapidMiner

Compared 7% of the time

Databricks vs Altair RapidMiner

Compared 7% of the time

IBM SPSS Statistics vs Altair RapidMiner

Compared 4% of the time

Microsoft Azure Machine Learning Studio vs Altair RapidMiner

Compared 3% of the time

More Altair RapidMiner Competitors

Databricks vs Alteryx

Compared 12% of the time

Dataiku vs Alteryx

Compared 9% of the time

KNIME Business Hub vs Alteryx

Compared 8% of the time

Anaplan vs Alteryx

Compared 3% of the time

IBM SPSS Statistics vs Alteryx

Compared 2% of the time

More Alteryx Competitors

Amazon SageMaker vs DataRobot

Compared 8% of the time

Cohere vs DataRobot

Compared 6% of the time

Datadog vs DataRobot

Compared 6% of the time

Microsoft Azure Machine Learning Studio vs DataRobot

Compared 5% of the time

DX Unified Infrastructure Management vs DataRobot

Compared 4% of the time

More DataRobot Competitors

Product Reports

Altair RapidMiner

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Download Alteryx product report

Download DataRobot product report

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

Alteryx provides user-friendly, no-code tools for data blending, preparation, and analysis. Its drag-and-drop interface and in-database capabilities simplify integration with data sources while maintaining data integrity.

Alteryx offers a comprehensive suite for automation of data workflows, reducing manual tasks and enhancing processing efficiency. Known for robust predictive and spatial analytics, it effectively handles large datasets. The platform's flexibility allows for custom script deployments, supported by a strong community. However, Alteryx faces challenges with high pricing, lack of cloud support, and limited data visualization tools. Users express a need for more in-built data science functionalities, improved API integration, and a smoother learning curve. Connectivity and documentation gaps, along with complex workflows, are noted concerns, suggesting areas for enhancement. Alteryx is widely used for tasks like ETL processes, data preparation, predictive modeling, and report generation, supporting functions like financial projections and spatial analysis.

What features define Alteryx?

Data Blending: Combines data from multiple sources.
No-Code Tools: User-friendly drag-and-drop interface for ease of use.
Automation: Streamlines workflows and reduces manual tasks.
Predictive Analytics: Offers advanced forecasting capabilities.
Spatial Analytics: Processes geospatial data effectively.

What benefits should users consider?

Reduced Manual Labor: Automation minimizes repetitive tasks.
Data Integration: Seamlessly connects to various data sources.
Customization: Flexibility to deploy custom scripts.
Scalability: Quickly processes large datasets.
Community Support: Strong network for troubleshooting and guidance.

Alteryx is implemented across industries for diverse needs such as anomaly detection in finance, customer segmentation in marketing, and tax automation in auditing. Teams leverage its capabilities for data blending and predictive modeling to enhance operational efficiency and address specific business needs effectively.

DataRobot automates model building and deployment, simplifying MLOps with user-friendly interfaces. Its AutoML and feature engineering streamline model comparison, selection, and testing, enhancing efficiency and scalability.

DataRobot facilitates efficient integration with cloud systems and data sources, reducing manual workload, enhancing productivity, and empowering data-driven decision-making. Its strengths lie in automating complex modeling tasks and supporting multiple predictive models effectively. Users emphasize the need for better handling of large datasets, integration with orchestration tools, and more flexibility for custom code integration and advanced model tuning. They also seek improved support response times, transparent model processing, real-world documentation, and enhanced capabilities in generative AI and accuracy metrics.

What are the key features of DataRobot?

Automated Modeling: Efficiently builds models with AutoML capabilities.
Model Deployment: Simplifies the deployment of machine learning models.
Model Monitoring: Tracks model performance for sustained accuracy.
Cloud System Integration: Seamlessly connects with major cloud platforms for scalability.
Data Source Compatibility: Integrates with various data sources to reduce manual efforts.

What benefits should users consider in reviews?

Increased Productivity: Automation reduces time spent on model development.
Scalability: Easily handles multiple models across departments.
Data-Driven Decisions: Empowers teams with actionable insights.
Time Efficiency: Significantly reduces project timelines.

DataRobot is adopted across industries like healthcare and education for creating and monitoring machine learning models. It accelerates development with GUI capabilities, aids data cleaning, and optimizes feature engineering and deployment. Organizations can predict behaviors, automate tasks, manage production models, and integrate into data science processes to improve data processing and maximize efficiency.