We performed a comparison between Alteryx and H2O.ai based on real PeerSpot user reviews.
Find out in this report how the two Data Science Platforms solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."The value add of Alteryx is the agility for making changes, and speed of deployment."
"The Alteryx designer has been the most useful feature in the solution."
"The most valuable feature of Alteryx is user-friendliness."
"I think the most valuable feature for Alteryx in a health facility is that it permits cleaning, organizing, and merging of databases such as Excel and Access."
"I like the solution's velocity, the speed with which it processes data, and its ease of use."
"I like that I can merge data from different sources into one place."
"Alteryx has made us more agile and increased the speed and effectiveness of decision making."
"The most valuable feature of Alteryx is its unlimited handling capabilities."
"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."
"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."
"It is helpful, intuitive, and easy to use. The learning curve is not too steep."
"AutoML helps in hands-free initial evaluations of efficiency/accuracy of ML algorithms."
"The ease of use in connecting to our cluster machines."
"Lacks an open source edition which would be helpful."
"I think they should really work on integrating or have a capacity to integrate some algorithmic code. I think that's one of the most important things they need to be doing."
"There's a big jump in terms of pricing between license tiers. I'm not sure I understand why the price jumps are so high."
"Alteryx's development environment could be improved as it requires installation locally and can't be developed in the cloud."
"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."
"Alteryx can improve the model management and deployment processing of large workloads."
"It would be great if Alteryx could take third party tools and incorporate them."
"Alteryx could be improved in the area of analytics and central governance."
"I would like to see more features related to deployment."
"Referring to bullet-3 as well, H2O DataFrame manipulation capabilities are too primitive."
"It needs a drag and drop GUI like KNIME, for easy access to and visibility of workflows."
"It lacks the data manipulation capabilities of R and Pandas DataFrames. We would kill for dplyr offloading H2O."
"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."
"On the topic of model training and model governance, this solution cannot handle ten or twelve models running at the same time."
Earn 20 points
Alteryx is ranked 3rd in Data Science Platforms with 74 reviews while H2O.ai is ranked 21st in Data Science Platforms. Alteryx is rated 8.4, while H2O.ai is rated 7.6. The top reviewer of Alteryx writes "Feature-rich ETL that condenses a number of functions into one tool". On the other hand, the top reviewer of H2O.ai writes "It is helpful, intuitive, and easy to use. The learning curve is not too steep". Alteryx is most compared with KNIME, Dataiku, Databricks, RapidMiner and Tableau, whereas H2O.ai is most compared with Databricks, Amazon SageMaker, Dataiku, Microsoft Azure Machine Learning Studio and SAS Visual Analytics. See our Alteryx vs. H2O.ai report.
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