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Alteryx vs Amazon SageMaker 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 Amazon SageMaker is 5.7%, down from 7.9% 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 (%)
Amazon SageMaker5.7%
Alteryx5.7%
H2O.ai1.7%
Other86.9%
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.
Saurabh Jaiswal - PeerSpot reviewer
Create innovative assistants with seamless data integration for large-scale projects
The various integration options available in Amazon SageMaker, such as Firehose for connecting to data pipelines, are simple to use. Tools like AWS Glue integrate well for data transformations. The Databricks integration aids data scientists and engineers. SageMaker is fully managed, offers high availability, flexibility with TensorFlow, PyTorch, and MXNet, and comes with pre-trained algorithms for forecasting, anomaly detection, and more.
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 most valuable feature of this solution is data preparation."
"All of the data science features in terms of unioning and joining data together are valuable."
"The most valuable feature is user-friendliness, as Alteryx can be used by those without any coding experience or experienced data scientists as it has the functionality to embed R and Python scripts."
"The GUI is simple and it integrates with Python."
"Alteryx makes it easy for the end customer to see clean data in a structured form."
"The product's most valuable features include its ease of use for non-technical users and machine learning capabilities."
"Alteryx has helped us spend more time identifying results instead of performing analysis manually. It has helped us in our loading process, including scrubbing data and identifying data elements that need to be corrected. It enables us to understand our data sets a lot better."
"This is a drag-and-drop tool which is easy-to-use and yet can be customized by creating your own components."
"The Autopilot feature is really good because it's helpful for people who don't have much experience with coding or data pipelines. When we suggest SageMaker to clients, they don't have to go through all the steps manually. They can leverage Autopilot to choose variables, run experiments, and monitor costs. The results are also pretty accurate."
"They offer insights into everyone making calls in my organization."
"Amazon SageMaker is highly valuable for managing ML workloads. It connects to AWS cloud resources, making it easy to deploy algorithms and collaborate using tools like GitLab. It offers a wide range of Python libraries and other necessary tools for modelling and algorithms."
"The solution is easy to scale...The documentation and online community support have been sufficient for us so far."
"Feature Store, CodeCommit, versioning, model control, and CI/CD pipelines are the most valuable features in Amazon SageMaker."
"Allows you to create API endpoints."
"The most valuable feature of Amazon SageMaker is that you don't have to do any programming in order to perform some of your use cases."
"I have seen a return on investment, probably a factor of four or five."
"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."
"The ease of use in connecting to our cluster machines."
"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 feature of H2O.ai is that it is plug-and-play."
"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."
"AutoML helps in hands-free initial evaluations of efficiency/accuracy of ML algorithms."
"It is helpful, intuitive, and easy to use. The learning curve is not too steep."
 

Cons

"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."
"I honestly can't think of anything that needs to be improved."
"If there is any way to make the learning curve less steep, that would be ideal."
"Alteryx's development environment could be improved as it requires installation locally and can't be developed in the cloud."
"There are a few hiccups with specific data sets and languages or formats that the data comes in. That may be a minor problem, but we can work through it. We had some issues looking at XML format in added data, but it wasn't significant."
"More statistics tools: We can use to compare SPSS statistics with some automated advisory."
"It would be great to create the final users' visualization within Alteryx."
"The event handling, such that the file system watcher, is in need of improvement."
"The main challenge with Amazon SageMaker is the integrations."
"Amazon SageMaker can make it simpler to manage the data flow from start to finish, such as by integrating data, usingthe machine, and deploying models. This process could be more user-friendly compared to other tools. I would also like to improve integration with Bedrock and the LLM connection for AWS."
"The dashboard could be improved by including more features and providing more information about deployed models, their drift, performance, scaling, and customization options."
"The user interface (UI) and user experience (UX) of SageMaker and AWS, in general, need improvement as they are not intuitive and require substantial time to learn how to use specific services."
"In general, improvements are needed on the performance side of the product's graphical user interface-related area since it consumes a lot of time for a user."
"The training modules could be enhanced. We had to take in-person training to fully understand SageMaker, and while the trainers were great, I think more comprehensive online modules would be helpful."
"There are other better solutions for large data, such as Databricks."
"AI is a new area and AWS needs to have an internship training program available."
"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."
"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."
"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."
"H2O.ai can improve in areas like multimodal support and prompt engineering."
"It lacks the data manipulation capabilities of R and Pandas DataFrames. We would kill for dplyr offloading H2O."
 

Pricing and Cost Advice

"The price could be better."
"The license price of the solution is expensive."
"It can be a bit pricey, especially after the first year."
"It is an expensive solution."
"Opt for the three year subscription. It is 20% less than the yearly one."
"In my opinion, it's actually quite expensive."
"It's very expensive. I'd rate it a four out of ten in terms of the price. It's great for big companies but not for small companies."
"In order to have designers, and, if you want to collaborate, you have to buy a server. If the designer is $5,000, and if you want a server, you have to pay $80,000."
"The support costs are 10% of the Amazon fees and it comes by default."
"The solution is relatively cheaper."
"The cost offers a pay-as-you-go pricing model. It depends on the instance that you do."
"SageMaker is worth the money for our use case."
"Amazon SageMaker is a very expensive product."
"You don't pay for Sagemaker. You only pay for the compute instances in your storage."
"The product is expensive."
"I would rate the solution's price a ten out of ten since it is very high."
"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
18%
Computer Software Company
11%
Manufacturing Company
9%
University
5%
Financial Services Firm
16%
Computer Software Company
14%
Manufacturing Company
9%
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 Business12
Midsize Enterprise11
Large Enterprise16
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...
How would you compare Databricks vs Amazon SageMaker?
We researched AWS SageMaker, but in the end, we chose Databricks. Databricks is a Unified Analytics Platform designe...
What do you like most about Amazon SageMaker?
We've had experience with unique ML projects using SageMaker. For example, we're developing a platform similar to Cha...
What is your experience regarding pricing and costs for Amazon SageMaker?
If you manage it effectively, their pricing is reasonable. It's similar to anything in the cloud; if you don't manage...
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
AWS SageMaker, SageMaker
No data available
 

Overview

 

Sample Customers

AnalyticsIq Inc., belk, BloominBrands Inc., Cardinalhealth, Cineplex, Dairy Queen
DigitalGlobe, Thomson Reuters Center for AI and Cognitive Computing, Hotels.com, GE Healthcare, Tinder, Intuit
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.
869,202 professionals have used our research since 2012.