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Dataiku vs Domino Data Science Platform vs KNIME Business Hub comparison

 

Comparison Buyer's Guide

Executive Summary

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 April 2026, in the Data Science Platforms category, the mindshare of Dataiku is 5.9%, down from 12.7% compared to the previous year. The mindshare of Domino Data Science Platform is 2.1%, down from 2.5% compared to the previous year. The mindshare of KNIME Business Hub is 5.8%, down from 11.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Science Platforms Mindshare Distribution
ProductMindshare (%)
Dataiku5.9%
KNIME Business Hub5.8%
Domino Data Science Platform2.1%
Other86.2%
Data Science Platforms
 

Featured Reviews

SK
Senior Data Scientist at Deloitte
Visual workflows have streamlined healthcare analytics and have reduced reporting time significantly
In terms of improvement, I cannot comment on the LLMs or the agentic view as I have not used them yet. However, I feel that better documentation is necessary. Dataiku should establish a stronger community since this is proprietary software, where users can share knowledge. Although they have some community interaction, it is often challenging to find assistance when stuck. For example, when I was new to Dataiku and trying to use an external optimization tool such as CPLEX, I struggled with resource directory linking to a project's notebook. Detailed documentation and community discussions could have significantly alleviated these issues for users such as myself.
AS
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…
NataliaRaffo - PeerSpot reviewer
Co Founder & Chief Data Officer Cdo at NTT DATA
Workflow automation has accelerated advanced analytics and machine learning delivery
Sometimes it is a little bit difficult to use some nodes when we have many large-scale data, for example, CSV files with a large amount of data. It is sometimes difficult to try to import the data in KNIME Business Hub nodes because I think that some features that are in the CSV in text, for example, large text, is difficult for KNIME Business Hub to import these fields. I don't know why, but it is very difficult. We need to try to use different nodes for importing the data, such as File Reader and CSV Reader. However, I think that it is always the features that have much text, it is difficult for KNIME Business Hub to understand and import this information. I don't know why, or maybe I don't know if we don't know what the better option is to configure the node to import all the CSV or the data set. However, we have always had this problem. In some nodes, sometimes it is the same because sometimes, for example, I have a CSV and in my CSV, I have a feature that is, for example, a date. When I import this data set in the File Reader node, I have problems with this field because it is a date, but the problem is that it imports it as text, for example. We try to use their nodes that convert text to date, but sometimes it is difficult, and it is not immediate to transform the text into a date. So we needed to convert the text into a date in the CSV, and then import it again in the KNIME Business Hub node and try to have a good read of this field. I know that KNIME Business Hub has some nodes to convert text to date and others, but sometimes it is difficult to use these nodes. I don't know why. Maybe it needs a specific format for the date and we need to transform our feature in this option. So sometimes it is a large process to convert these features. However, sometimes we need to investigate and search for other nodes, and try with other nodes to import these cases.

Quotes from Members

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

Pros

"I believe the return on investment looks positive."
"Cloud-based process run helps in not keeping the systems on while processes are running."
"The best features Dataiku offers that help me with my demand forecasting and data science projects include having a complete overview of the flow directly from the flowchart, allowing me to observe all the steps in a single overview, and the ability to use a no-code, low-code node."
"I rate the overall product as eight out of ten."
"The advantage is that you can focus on machine learning while having access to what they call 'recipes.' These recipes allow me to preprocess and prepare data without writing any code."
"The most valuable feature of this solution is that it is one tool that can do everything, and you have the ability to very easily push your design to prediction."
"The best feature in Dataiku is that once the data is connected in the underneath layer, it flows exceptionally smoothly if you know how to tweak it."
"Dataiku has positively impacted my organization, specifically in one project where we performed migration from AWS to Dataiku, speeding up the solution by close to 40% and reducing architecture costs by almost 70%, which was a significant benefit and greatly impacted our operations."
"The workspaces, which are like wrappers of Docker containers, made it easy to start development environments using Domino."
"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."
"This solution is easy to use and especially good at data preparation and wrapping."
"It's very convenient to write your own algorithms in KNIME; you can write it in Java script or Python transcript."
"The most useful features are the readily available extensions that speed up the work."
"So far, it can solve my data analysis problems and I think it's a powerful data analysis tool."
"KNIME has improved our organization because we are able to collect data in a way that we can interpret and it provides visuals."
"From a user-friendliness perspective, it's a great tool."
"Stability is excellent. I would give it a nine out of ten."
"I use it personally for my purposes and for the company; I use it for internal data science with very good results."
 

Cons

"I have not seen a return on investment with Dataiku in terms of time saved, money saved, or fewer employees needed."
"Dataiku still needs some coding, and that could be a difference where business data scientists would go for DataRobot more than Dataiku."
"Dataiku is down a lot of times, and we have to wait for sometimes five, ten, or fifteen minutes, after which it gets working again, and during those times, we are unable to get our work done."
"I find that it is a little slow during use. It takes more time than I would expect for operations to complete."
"All products have room for improvement, and I would like to see their pricing simplified, as it is somewhat complex."
"One of the main challenges was collaboration. Developers typically use GitHub to push and manage code, but integrating GitHub with Dataiku was complicated."
"Although known for Big Data, the processing time to process 1.8 billion records was terribly slow (five days)."
"I think it would help if Data Science Studio added some more features and improved the data model."
"The predictive analysis feature needs improvement."
"The predictive analysis feature needs improvement."
"The deployment of large language models (LLMs) could be improved."
"There should be better documentation and the steps should be easier."
"The ability to handle large amounts of data and performance in processing need to be improved."
"There are a lot of tools in the product and it would help if they were grouped into classes where you can select a function, rather than a specific tool."
"The program is not fit for handling very large files or databases (greater than 1GB); it gets too slow and has a tendency to crash easily."
"KNIME is not scalable."
"There are some parameters that I would like to have at a bigger scale. The upper limit of one node that tries to find spots or areas in photos was too small for us. It would need to be bigger."
"The documentation needs a proper rework."
"The current UI is primarily in English. Analyzing data in local languages might present challenges or issues."
 

Pricing and Cost Advice

"Pricing is pretty steep. Dataiku is also not that cheap."
"The annual licensing fees are approximately €20 ($22 USD) per key for the basic version and €40 ($44 USD) per key for the version with everything."
Information not available
"KNIME Business Hub is expensive for small companies."
"KNIME is an open-source tool, so it's free to use."
"The price for Knime is okay."
"Scaling to the on-premises version requires a licensing fee per user that is a bit expensive in comparison to R, Python, and SAS."
"I use the tool's free version."
"They have different versions, but I am using the open-source one."
"This is a free open-source solution."
"KNIME is free as a stand-alone desktop-based platform but if you want to get a KNIME server then you can find the cost on their website."
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Top Industries

By visitors reading reviews
Financial Services Firm
19%
Computer Software Company
9%
Manufacturing Company
9%
Energy/Utilities Company
6%
Financial Services Firm
36%
Manufacturing Company
9%
Insurance Company
8%
Healthcare Company
5%
Financial Services Firm
12%
Manufacturing Company
9%
University
8%
Educational Organization
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business6
Midsize Enterprise2
Large Enterprise13
No data available
By reviewers
Company SizeCount
Small Business21
Midsize Enterprise16
Large Enterprise31
 

Questions from the Community

What is your experience regarding pricing and costs for Dataiku Data Science Studio?
The licenses are a bit high for companies that are still hesitating to get started with using Dataiku. For my persona...
What needs improvement with Dataiku Data Science Studio?
I have no suggestions for improvements because it's all good; it just sometimes lags a lot, and I don't know if the s...
What is your primary use case for Dataiku Data Science Studio?
My main use case for Dataiku involves ETL pipelines, mainly for data analysis, and I majorly use SQL queries for that...
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 can...
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-e...
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 so...
What is your experience regarding pricing and costs for KNIME?
I rate the product’s pricing a seven out of ten, where one is cheap and ten is expensive.
What needs improvement with KNIME?
Regarding integration capabilities, I do not think it is that easy to integrate KNIME Business Hub with another produ...
What is your primary use case for KNIME?
My use case for KNIME Business Hub includes automation, querying from the database, and outputting to Excel and creat...
 

Also Known As

Dataiku DSS
Domino Data Lab Platform
KNIME Analytics Platform
 

Interactive Demo

Demo not available
Demo not available
 

Overview

 

Sample Customers

BGL BNP Paribas, Dentsu Aegis, Link Mobility Group, AramisAuto
Allstate, GSK, AstraZeneca, Federal Reserve, US Navy, Bristol Myers Squibb, Bayer, BNP Paribas, Moodys, New York Life
Infocom Corporation, Dymatrix Consulting Group, Soluzione Informatiche, MMI Agency, Estanislao Training and Solutions, Vialis AG
Find out what your peers are saying about Databricks, Dataiku, Amazon Web Services (AWS) and others in Data Science Platforms. Updated: April 2026.
889,955 professionals have used our research since 2012.