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Alteryx 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

Alteryx
Ranking in Data Science Platforms
5th
Average Rating
8.4
Reviews Sentiment
7.0
Number of Reviews
83
Ranking in other categories
Predictive Analytics (1st), Data Preparation Tools (1st)
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 November 2025, in the Data Science Platforms category, the mindshare of Alteryx is 5.2%, down from 6.9% compared to the previous year. The mindshare of H2O.ai is 1.7%, up from 1.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Science Platforms Market Share Distribution
ProductMarket Share (%)
Alteryx5.2%
H2O.ai1.7%
Other93.1%
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.
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 solution offers excellent predicting power. The accuracy and confidence have been great."
"The drag-and-drop features are useful for data scientists who do not like to code because it is already in the system."
"Alteryx is user-friendly and allows easy creation of workflows compared to Informatica PowerCenter."
"Alteryx is user-friendly and allows easy creation of workflows compared to Informatica PowerCenter."
"Technical support is very helpful."
"Good data transformation."
"The feature that I have found most valuable for Alteryx is its geo-referencing feature, it is very good. I use it a lot, especially for supply chain."
"It is one of the most complete and great customer service that I experienced with a software company/ecosystem."
"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."
"AutoML helps in hands-free initial evaluations of efficiency/accuracy of ML algorithms."
"The most valuable feature of H2O.ai is that it is plug-and-play."
"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."
"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."
"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 ease of use in connecting to our cluster machines."
 

Cons

"Based on my experience, I would not recommend Alteryx to others. I would encourage developing internal capabilities over using Alteryx as a solution."
"There are a few imputation techniques which they really need to include."
"The screen when you are looking into your workflows and your ETL processes needs to be improved. You cannot manage it very well."
"There were times when the product would fail during development without an apparent reason."
"From a commercial standpoint, more options should be available for pricing. The licensing model needs to be specific."
"I think sometimes the solution doesn't load properly or takes so much time for the workflows. Though the workflow runs and completes the file in Excel, if you use the same formula, it's a bit slow. Also, the image processing is not so good because I tried to do some image processing and they were like, sometimes they put two to eight. In the document, it was two, but the OCR predicted it as eight."
"It should have Linux support. It currently supports only Windows. It does not support any other platform. Its price should also be lower. Their US partner management program is actually unresponsive. One of the reasons why we don't have a formalized partnership plan with them is because their partner management team is atrociously unresponsive. This is something that they need to change."
"The scalability worries me a lot at the moment because the cost of the solution is high and it is not growing considerably. Unfortunately, we started during early COVID times and because of the economic downturn, all the cost cuttings, and everything, it is not growing as expected."
"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."
"On the topic of model training and model governance, this solution cannot handle ten or twelve models running at the same time."
"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."
"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."
 

Pricing and Cost Advice

"A designer and scheduler for $13K/year in total is pretty much earning you the money back in time and in other resources."
"I rate the solution's pricing as a ten, as it is highly priced."
"It's probably on the pricey side, but they provide some really useful ways to grow and test."
"The designer license costs 5000 euros. The server edition is 1000 euros."
"It can be a bit pricey, especially after the first year."
"The pricing is $5000 per year per production license."
"Alteryx is generally more suited for medium—to large companies due to its potentially high licensing costs."
"The license price of the solution is expensive."
"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
23%
Manufacturing Company
9%
Computer Software Company
8%
Retailer
6%
Financial Services Firm
16%
Computer Software Company
14%
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 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 directly if you want to know more.
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, R integrations if your team requires this. It can handle over 2 billion rows of...
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 the following: - An excellent desktop tool for Data Prep and analytics. - Featu...
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 to work with fusion models, H2O.ai doesn't provide that kind of information. Cu...
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 which model was performing better for job portal data. At that time, H2O.ai was ev...
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 individual models, you need to train and test each one. With H2O.ai, these concer...
 

Comparisons

 

Overview

 

Sample Customers

AnalyticsIq Inc., belk, BloominBrands Inc., Cardinalhealth, Cineplex, Dairy Queen
poder.io, Stanley Black & Decker, G5, PWC, Comcast, Cisco
Find out what your peers are saying about Alteryx vs. H2O.ai and other solutions. Updated: September 2025.
873,209 professionals have used our research since 2012.