No more typing reviews! Try our Samantha, our new voice AI agent.

Dataiku vs H2O.ai 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 June 2026, in the Data Science Platforms category, the mindshare of Dataiku is 5.2%, down from 13.0% compared to the previous year. The mindshare of H2O.ai is 2.6%, up from 1.7% compared to the previous year. The mindshare of KNIME Business Hub is 5.1%, down from 12.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Science Platforms Mindshare Distribution
ProductMindshare (%)
Dataiku5.2%
KNIME Business Hub5.1%
H2O.ai2.6%
Other87.1%
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.
MA
Senior Manager - AI at Shamal Holding
Have improved machine learning model automation and reduced decision-making time
One improvement I would like to see in H2O.ai is regarding the integration capabilities with different data sources, as I've seen platforms like DataIQ and DataBricks offer great integration with various data sources. H2O.ai could benefit from enhanced integration with real-time versus offline data sources, as well as improvements in productionalization solutions, including better deployment options on platforms like Azure and CI/CD integration. One of the features I'd like to see included in upcoming releases of H2O.ai pertains to the growing trend of Generative AI, with applications for LLM-based models and vector databases. I would like to see a solution similar to Azure AI Foundry, which provides the flexibility to integrate different LLMs into applications, including H2O-GPT and other models for varied applications.
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

"One of the valuable features of Dataiku is the workflow capability."
"The best features Dataiku offers include the ability for users to use the node without having to code and the functionality related to low-code/no-code."
"I rate the overall product as eight out of ten."
"Technical support is really, really good."
"The solution is quite stable."
"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."
"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."
"Dataiku is highly regarded as it is a leader in the Gartner ranking."
"It is helpful, intuitive, and easy to use. The learning curve is not too steep."
"H2O.ai provides better flexibility where I could examine more models and obtain results, and based on these results, I could make the next set of decisions."
"The product is definitely worth looking at, as it is one of the upcoming products where you can build large models for use cases."
"We have seen significant ROI where we were able to use the product in certain key projects and could automate a lot of processes."
"The company is interested in using an external platform in order to have an updated environment."
"The most valuable feature of H2O.ai is that it is plug-and-play."
"One of the most interesting features of the product is their driverless component, which allows you to test several different algorithms along with navigating you through choosing the best algorithm and gives you an interpretability capability that allows you to have some understanding of what's inside the algorithm and why it's behaving a certain way, making sure you are not biased towards the outcome."
"I have utilized the AutoML feature in H2O.ai, which is one of the very powerful features where you don't need to worry about which algorithm is best for your model."
"Key features include: very easy-to-use visual interface; Help functions and clear explanations of the functionalities and the used algorithms; Data Wrangling and data manipulation functionalities are certainly sufficient, as well as the looping possibilities which help you to automate parts of the analysis."
"From a user-friendliness perspective, it's a great tool."
"I've tried to utilize KNIME to the fullest extent possible to replace Excel."
"I use it personally for my purposes and for the company; I use it for internal data science with very good results."
"The tool's analytic capabilities are good."
"For organizations with a small team of data analysts or data scientists, it is a very easy tool to become familiar with predictive modeling, and makes it possible to hand over projects to colleagues without the need to extensively document them."
"Easy to connect with every database: We use queries from SQL, Redshift, Oracle."
"This open-source product can compete with category leaders in ELT software."
 

Cons

"I think the pricing and licensing of Dataiku is a bit expensive; it could be improved further, and I think they should have a different kind of licensing model as well."
"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."
"There is room for improvement in terms of allowing for more code-based features."
"I think it would help if Data Science Studio added some more features and improved the data model."
"I need to stress upon the part about customer support because there are some product issues we have identified and raised with customer support, but sometimes the response is delayed, so that can be improved."
"Dataiku still needs some coding, and that could be a difference where business data scientists would go for DataRobot more than Dataiku."
"Maybe on the interface in general, the information can easily get lost."
"I find that it is a little slow during use. When I use Dataiku to run my script to transfer data, it takes more time than I would expect for the operation to complete."
"The interpretability module has room for improvement. Also, it needs to improve its ability to integrate with other systems, like SageMaker, and the overall integration capability."
"The model management features could be improved."
"Referring to bullet-3 as well, H2O DataFrame manipulation capabilities are too primitive."
"Feature engineering."
"It lacks the data manipulation capabilities of R and Pandas DataFrames. We would kill for dplyr offloading H2O."
"I would like to see more features related to deployment."
"H2O.ai can improve in areas like multimodal support and prompt engineering."
"It needs a drag and drop GUI like KNIME, for easy access to and visibility of workflows."
"The diversity of native algorithms could be improved."
"The ability to handle large amounts of data and performance in processing need to be improved."
"For graphics, the interface is a little confusing. So, this is a point that could be improved."
"One thing to consider is that the prebuilt nodes may not always be a perfect fit for your specific needs, although most of the time, they work quite well."
"It's difficult to pinpoint one single feature because KNIME has so many. For starters, it's very easy to learn. You can get started with just a one-hour video. The drag-and-drop interface makes it user-friendly. For example, if you want to read an Excel file, drag the "read Excel file" node from the repository, configure it by specifying the file location, and run it. This gives you a table with all your data."
"They could add more detailed examples of the functionality of every node, how it works and how we can use it, to make things easier at the beginning."
"In the last update, KNIME started hiding a lot of the nodes. It doesn't mean hiding, but you need to know what you're looking for. Before that, you had just a tree that you could click, and you could get an overview of what kind of nodes do I have. Right now, it's like you need to know which node you need, and then you can start typing, but it's actually more difficult to find them."
"KNIME could improve when it comes to large data markets."
 

Pricing and Cost Advice

"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."
"Pricing is pretty steep. Dataiku is also not that cheap."
"We have seen significant ROI where we were able to use the product in certain key projects and could automate a lot of processes. We were even able to reduce staff."
"It is free of cost. It is GNU licensed."
"While there are certain limitations in functionality, you can still utilize it efficiently free of charge."
"It is expensive to procure the license."
"There is no cost for using KNIME because it is an open-source solution, but you have to pay if you need a server."
"KNIME is free and open source."
"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."
"KNIME is a cost-effective solution because it’s free of cost."
"They have different versions, but I am using the open-source one."
report
Use our free recommendation engine to learn which Data Science Platforms solutions are best for your needs.
899,125 professionals have used our research since 2012.
 

Top Industries

By visitors reading reviews
Financial Services Firm
19%
Manufacturing Company
10%
Computer Software Company
9%
Energy/Utilities Company
6%
Financial Services Firm
21%
Computer Software Company
8%
Manufacturing Company
7%
Construction Company
6%
Financial Services Firm
12%
Manufacturing Company
9%
University
8%
Educational Organization
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business6
Midsize Enterprise2
Large Enterprise13
By reviewers
Company SizeCount
Small Business2
Midsize Enterprise3
Large Enterprise7
By reviewers
Company SizeCount
Small Business21
Midsize Enterprise16
Large Enterprise32
 

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 H2O.ai?
Even though H2O.ai provides the best model, there could be improvements in certain areas. For instance, when you want...
What is your primary use case for H2O.ai?
I used H2O.ai on several POCs for my previous company, and it helped me find the best model. I needed to determine wh...
What advice do you have for others considering H2O.ai?
For larger datasets, model computation or model training and testing typically takes considerable time because with i...
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?
I would describe KNIME Decision Hub as somewhat helpful in making data-driven decisions more efficient. It could have...
What is your primary use case for KNIME?
I mainly use KNIME Business Hub currently for data ETLs and then it meets with predictive analytics. Sometimes I util...
 

Comparisons

 

Also Known As

Dataiku DSS
No data available
KNIME Analytics Platform
 

Overview

 

Sample Customers

BGL BNP Paribas, Dentsu Aegis, Link Mobility Group, AramisAuto
poder.io, Stanley Black & Decker, G5, PWC, Comcast, Cisco
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, Knime and others in Data Science Platforms. Updated: May 2026.
899,125 professionals have used our research since 2012.