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Dataiku vs H2O.ai comparison

 

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

Dataiku
Ranking in Data Science Platforms
6th
Average Rating
8.2
Reviews Sentiment
7.1
Number of Reviews
12
Ranking in other categories
No ranking in other categories
H2O.ai
Ranking in Data Science Platforms
16th
Average Rating
7.6
Reviews Sentiment
6.8
Number of Reviews
10
Ranking in other categories
Model Monitoring (4th)
 

Mindshare comparison

As of August 2025, in the Data Science Platforms category, the mindshare of Dataiku is 12.9%, up from 9.7% compared to the previous year. The mindshare of H2O.ai is 1.8%, up from 1.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Science Platforms
 

Featured Reviews

RichardXu - PeerSpot reviewer
The platform organizes workflows visually and efficiently
One of the valuable features of Dataiku is the workflow capability. It allows us to organize a workflow efficiently. The platform has a visual interface, making it much easier for educated professionals to organize their work. This feature is useful because it simplifies tasks and eliminates the need for a data scientist. If you are knowledgeable about AI, you can directly write using primitive tools like Pantera flow, PyTorch, and Scikit-learn. However, Dataiku makes this process much easier.
Abhay Vyas - PeerSpot reviewer
Advanced model selection and time efficiency meet needs but documentation and fusion model support are needed
Even though H2O.ai provides the best model, there could be improvements in certain areas. For instance, when you want to work with fusion models, H2O.ai doesn't provide that kind of information. Currently, it provides individual models as outcomes. If it could offer combinations of models, such as suggesting using XGBoost along with SVM for wonderful results, that fusion model concept would be a good option for developers. I hope the fusion model concept will be implemented soon in H2O.ai. Regarding documentation, I faced challenges as I didn't see much information from a documentation perspective. When I was trying to learn how to train and test H2O.ai, there was limited documentation available. If they could improve in that area, it would be really beneficial.

Quotes from Members

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

Pros

"Extremely easy to use with its GUI-based functionality and large compatibility with various data sources. Also, maintenance processes are much more automated than ever, with fewer errors."
"I believe the return on investment looks positive."
"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."
"Data Science Studio's data science model is very useful."
"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."
"Cloud-based process run helps in not keeping the systems on while processes are running."
"One of the valuable features of Dataiku is the workflow capability."
"I like the interface, which is probably my favorite part of the solution. It is really user-friendly for an IT person."
"It is helpful, intuitive, and easy to use. The learning curve is not too steep."
"The ease of use in connecting to our cluster machines."
"AutoML helps in hands-free initial evaluations of efficiency/accuracy of ML algorithms."
"Fast training, memory-efficient DataFrame manipulation, well-documented, easy-to-use algorithms, ability to integrate with enterprise Java apps (through POJO/MOJO) are the main reasons why we switched from Spark to H2O."
"The most valuable features are the machine learning tools, the support for Jupyter Notebooks, and the collaboration that allows you to share it across people."
"One of the most interesting features of the product is their driverless component. The driverless component allows you to test several different algorithms along with navigating you through choosing the best algorithm."
"The most valuable feature of H2O.ai is that it is plug-and-play."
"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."
 

Cons

"Server up-time needs to be improved. Also, query engines like Spark and Hive need to be more stable."
"In the next release of this solution, I would like to see deep learning better integrated into the tool and not simply an extension or plugin."
"The ability to have charts right from the explorer would be an improvement."
"The technical support from Dataiku is not good. The support team does not provide adequate assistance, and there are concerns about billing requests."
"There is room for improvement in terms of allowing for more code-based features."
"The interface for the web app can be a bit difficult. It needs to have better capabilities, at least for developers who like to code. This is due to the fact that everything is enabled in a single window with different tabs. For them to actually develop and do the concurrent testing that needs to be done, it takes a bit of time. That is one improvement that I would like to see - from a web app developer perspective."
"I think it would help if Data Science Studio added some more features and improved the data model."
"The license is very expensive. It would be great to have an intermediate license for basic treatments that do not require extensive experience."
"The model management features could be improved."
"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."
"Referring to bullet-3 as well, H2O DataFrame manipulation capabilities are too primitive."
"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."
"It lacks the data manipulation capabilities of R and Pandas DataFrames. We would kill for dplyr offloading H2O."
"On the topic of model training and model governance, this solution cannot handle ten or twelve models running at the same time."
 

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."
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Top Industries

By visitors reading reviews
Financial Services Firm
18%
Manufacturing Company
10%
Computer Software Company
9%
Energy/Utilities Company
6%
Computer Software Company
17%
Financial Services Firm
17%
Manufacturing Company
9%
Educational Organization
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

What is your experience regarding pricing and costs for Dataiku Data Science Studio?
I find the pricing of Dataiku quite affordable for our customers, as they are usually large companies. However, it is a pricey solution and I primarily recommend it to bigger companies.
What needs improvement with Dataiku Data Science Studio?
There is room for improvement in terms of allowing for more code-based features. I would love for Dataiku to allow more flexibility with code-based components and provide the possibility to extend ...
What is your primary use case for Dataiku Data Science Studio?
My company sells licenses for both Dataiku and Alteryx, and we have clients who use them. I engage with several companies in telecommunications, retail, and energy to assess how our clients are uti...
What needs improvement with H2O.ai?
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 v...
What is your primary use case for H2O.ai?
Normally, I use H2O.ai for my machine learning tasks, and to give an example, some of the models that I've created using H2O.ai are taxi demand forecasting and a scoring model for leads. Most of my...
What advice do you have for others considering H2O.ai?
I would rate the technical support a nine. For organizations considering H2O.ai, my recommendations include appreciating it as a great and flexible tool for machine learning tasks without incurring...
 

Comparisons

 

Also Known As

Dataiku DSS
No data available
 

Overview

 

Sample Customers

BGL BNP Paribas, Dentsu Aegis, Link Mobility Group, AramisAuto
poder.io, Stanley Black & Decker, G5, PWC, Comcast, Cisco
Find out what your peers are saying about Dataiku vs. H2O.ai and other solutions. Updated: July 2025.
865,295 professionals have used our research since 2012.