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Alteryx vs Dataiku vs H2O.ai 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 October 2025, in the Data Science Platforms category, the mindshare of Alteryx is 5.7%, down from 7.2% compared to the previous year. The mindshare of Dataiku is 11.7%, up from 10.9% compared to the previous year. The mindshare of H2O.ai is 1.7%, up from 1.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Science Platforms Market Share Distribution
ProductMarket Share (%)
Alteryx5.7%
Dataiku11.7%
H2O.ai1.7%
Other80.9%
Data Science Platforms
 

Featured Reviews

Theresa McLaughlin - PeerSpot reviewer
Quick development enables seamless data processing despite occasional support issues
There were times when the product would fail during development without an apparent reason. The support structure changed; initially, we received great support, however, it later became less reliable due to licensing issues and a tiered support system. Licensing negotiations were problematic, affecting our product usage. For instance, our licenses were temporarily lost during negotiations when an agreement couldn't be reached.
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

"The product's Macros probably are one of the most useful aspects."
"The modeling features are very good."
"Automation is the most valuable aspect for us. The ability to wrap business logic around the data is very helpful."
"The most valuable feature of this solution is data preparation."
"Once we got it in-house, I knew its value."
"It is efficient in optimizing our ability to get information."
"Predictive models, which are easy to use, and help a lot with fast design and deployment​."
"All of the data science features in terms of unioning and joining data together are valuable."
"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."
"Traceability is vital since I manage many cohorts, and collaboration is key as I have multiple engineers substituting for one another."
"One of the valuable features of Dataiku is the workflow capability."
"Dataiku is highly regarded as it is a leader in the Gartner ranking."
"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."
"Data Science Studio's data science model is very useful."
"I believe the return on investment looks positive."
"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."
"It is helpful, intuitive, and easy to use. The learning curve is not too steep."
"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."
"The ease of use in connecting to our cluster machines."
"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."
"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."
"AutoML helps in hands-free initial evaluations of efficiency/accuracy of ML algorithms."
 

Cons

"Alteryx can improve in data science. They have to have more features and components in the data science aspect because they claim to be a data science tool. However, in order to be more competitive, they have to improve on their data science propositions. Thre are other solutions on the market, such as other players in the market, Data2Go or DataIQ, and Alteryx needs to catch up."
"The server is too expensive for what you get and it really a designer desktop on a server."
"The next feature release should include easier reporting."
"The solution can be made more affordable."
"Sometimes workflows tend to queue up, and they tend to get canceled for some reason that we don't know sometimes."
"We can't browse multiple files. When we deploy a solution on a gallery, let's say I have ten different files, and I have to upload them all at once. This is something that's difficult in the gallery. So case by case, I see some downsides, but often we do something alternative."
"They should make the solution user-friendly for nontechnical people by giving specific names to the options."
"We are hoping that the NLP features will also support Chinese characters."
"In terms of enhancing collaboration within my team, I would not say Dataiku is the best one because it's so expensive."
"There is room for improvement in terms of allowing for more code-based features."
"I find that it is a little slow during use. It takes more time than I would expect for operations to complete."
"Although known for Big Data, the processing time to process 1.8 billion records was terribly slow (five days)."
"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."
"One of the main challenges was collaboration. Developers typically use GitHub to push and manage code, but integrating GitHub with Dataiku was complicated."
"The license is very expensive."
"The ability to have charts right from the explorer would be an improvement."
"It lacks the data manipulation capabilities of R and Pandas DataFrames. We would kill for dplyr offloading H2O."
"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."
"The model management features could be improved."
"Referring to bullet-3 as well, H2O DataFrame manipulation capabilities are too primitive."
"On the topic of model training and model governance, this solution cannot handle ten or twelve models running at the same time."
"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."
 

Pricing and Cost Advice

"ROI is huge. There are some secondary benefits, like analysts getting their post 5 PM time back or the ability to shorten all closing processes to a half or less."
"We use the free version of the solution. There are enterprise licenses available. It cost approximately $5,000 annually. It is an expensive solution and there are additional features that cost more money."
"The license price of the solution is expensive."
"The solution has a more costly license than other tools in the market."
"The designer has a list price of $5,995 USD."
"The seat is too expensive."
"The cost of the solution could be reduced."
"The price could be better."
"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
22%
Manufacturing Company
9%
Computer Software Company
8%
Retailer
6%
Financial Services Firm
17%
Manufacturing Company
10%
Computer Software Company
9%
Energy/Utilities Company
6%
Financial Services Firm
16%
Computer Software Company
15%
Manufacturing Company
8%
Educational Organization
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business31
Midsize Enterprise15
Large Enterprise51
By reviewers
Company SizeCount
Small Business4
Midsize Enterprise1
Large Enterprise8
By reviewers
Company SizeCount
Small Business2
Midsize Enterprise3
Large Enterprise7
 

Questions from the Community

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 Dataiku Data Science Studio?
I find the pricing of Dataiku quite affordable for our customers, as they are usually large companies. However, it is...
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 mo...
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 compa...
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...
 

Comparisons

 

Also Known As

No data available
Dataiku DSS
No data available
 

Overview

 

Sample Customers

AnalyticsIq Inc., belk, BloominBrands Inc., Cardinalhealth, Cineplex, Dairy Queen
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 Databricks, Amazon Web Services (AWS), Knime and others in Data Science Platforms. Updated: September 2025.
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