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Cloudera Data Science Workbench vs Domino Data Science Platform comparison

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Comparison Buyer's Guide

Executive SummaryUpdated on Dec 5, 2024

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

Cloudera Data Science Workb...
Ranking in Data Science Platforms
24th
Average Rating
7.0
Reviews Sentiment
6.9
Number of Reviews
2
Ranking in other categories
No ranking in other categories
Domino Data Science Platform
Ranking in Data Science Platforms
17th
Average Rating
8.2
Reviews Sentiment
6.0
Number of Reviews
4
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of October 2026, in the Data Science Platforms category, the mindshare of Cloudera Data Science Workbench is 1.6%, up from 1.3% compared to the previous year. The mindshare of Domino Data Science Platform is 1.8%, down from 2.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Science Platforms Mindshare Distribution
ProductMindshare (%)
Domino Data Science Platform1.8%
Cloudera Data Science Workbench1.6%
Other96.6%
Data Science Platforms
 

Featured Reviews

Ismail Peer - PeerSpot reviewer
Program Management Lead Advisor at Unionbank Philippines
Useful for data science modeling but improvement is needed in MLOps and pricing
If you don't configure CDSW well, then it might be not useful for you. Deploying the tool can vary in complexity, but most of the time, it's relatively simple and straightforward. Triggering a job from data to production is easy, as the platform automates the deployment process. However, ensuring optimal resource allocation is essential for smooth operations.
reviewer2906820 - PeerSpot reviewer
Partner, AI and Machine Learning at a consultancy with 11-50 employees
Portable code has empowered cross‑client data science and now supports collaborative AI projects
Machine learning AI platforms like Domino Data Science Platform have the problem that they need to demonstrate well to citizen data scientists and technology management, so they need to make AI look easy. They also need to demonstrate well to professional data scientists who want to have their hands on as much power and scalability as possible. Lastly, they need to serve what we call MLOps engineers and software engineers to take the models and move them to production deployment. Those three personalities want a tool to look three different ways, and Domino Data Science Platform does a really good job at that. Domino Data Science Platform makes it accessible to citizen data scientists with basic concepts for IT management, has robust abilities to serve professional data scientists, and also has abilities to help MLOps and DataOps people deploy. I think the ability to select your environment, your containers, and your scalability, to use lots of computing memory when you need it, and then build your model, create your architectures, and then scale back to different sizes of the platforms as you need it is the area that Domino Data Science Platform stands out the most for professional data scientists. The ability to do the code yourself in Python and at the same time have automatic version control is really helpful too. I would highlight that Domino Data Science Platform is closer to Databricks, and while I have also used Dataiku, I would say that Dataiku is actually closer to the citizen data scientist platform.

Quotes from Members

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

Pros

"I appreciate CDSW's ability to logically segregate environments, such as data, DR, and production, ensuring they don't interfere with each other. The deployment of machine learning is fast and easy to manage. Its API calls are also fast."
"The Cloudera Data Science Workbench is customizable and easy to use."
"We primarily use the solution for customer retention, but there are a lot of use cases for this particular product."
"The scalability of the solution is good; I'd rate it four out of five."
"In terms of my experience deploying models using Domino Data Science Platform, I have previously worked with Google Cloud and Vertex AI, as well as Azure Machine Learning Studio, but I find that Domino Data Lab is a much more advanced platform specifically tailored for model deployment, which significantly eases our workflow."
"The workspaces, which are like wrappers of Docker containers, made it easy to start development environments using Domino."
"Domino Data Science Platform makes it accessible to citizen data scientists with basic concepts for IT management, has robust abilities to serve professional data scientists, and also has abilities to help MLOps and DataOps people deploy."
 

Cons

"Running this solution requires a minimum of 12GB to 16GB of RAM."
"We found this solution a little bit difficult to scale."
"The tool's MLOps is not good. It's pricing also needs to improve."
"The deployment of large language models (LLMs) could be improved."
"Improvement areas for Domino Data Science Platform could relate to resource monitoring capabilities; adding visuals that stakeholders can review would enhance awareness of resource usage and its impact on applications and costs."
"Regarding Domino Data Science Platform's AI capabilities, I find its governance and security to be basic at present."
"The predictive analysis feature needs improvement."
 

Pricing and Cost Advice

"The product is expensive."
Information not available
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Top Industries

By visitors reading reviews
Financial Services Firm
27%
Outsourcing Company
12%
Manufacturing Company
6%
Healthcare Company
6%
Financial Services Firm
34%
Manufacturing Company
9%
Insurance Company
8%
Comms Service Provider
4%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
No data available
 

Questions from the Community

Ask a question
Earn 20 points
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.
What is your primary use case for Domino Data Science Platform?
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...
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...
 

Also Known As

CDSW
Domino Data Lab Platform
 

Interactive Demo

Demo not available
 

Overview

 

Sample Customers

IQVIA, Rush University Medical Center, Western Union
Allstate, GSK, AstraZeneca, Federal Reserve, US Navy, Bristol Myers Squibb, Bayer, BNP Paribas, Moodys, New York Life
Find out what your peers are saying about Cloudera Data Science Workbench vs. Domino Data Science Platform and other solutions. Updated: September 2026.
917,433 professionals have used our research since 2012.