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H2O.ai vs SAS Visual Analytics 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:
 

Categories and Ranking

H2O.ai
Average Rating
7.6
Reviews Sentiment
7.2
Number of Reviews
9
Ranking in other categories
Data Science Platforms (20th), Model Monitoring (5th)
SAS Visual Analytics
Average Rating
8.2
Reviews Sentiment
7.0
Number of Reviews
40
Ranking in other categories
Data Visualization (6th)
 

Mindshare comparison

While both are Business Intelligence solutions, they serve different purposes. H2O.ai is designed for Data Science Platforms and holds a mindshare of 1.8%, up 1.4% compared to last year.
SAS Visual Analytics, on the other hand, focuses on Data Visualization, holds 3.5% mindshare, down 5.8% since last year.
Data Science Platforms
Data Visualization
 

Featured Reviews

Kashif Yaseen - PeerSpot reviewer
Plug-and-play convenience enhances productivity but needs better multimodal support
We mostly used the solution in the domain that I'm working. We had most of the use cases around chatbots and conversational BI The solution was plug-and-play, meaning most of the components were handled by the solution itself rather than building them from scratch. This was useful for our banking…
Danna Nemeth - PeerSpot reviewer
Provides ease of use and has advanced data visualization capabilities
The initial setup can be complex and depends on the specific environment and licensing. It typically requires some time and effort to configure properly. It can be deployed as a SaaS solution or on-premises. It typically requires an installer and someone knowledgeable in business analytics and data management. Generally, it does not require extensive maintenance, though occasional upkeep might be necessary.

Quotes from Members

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

Pros

"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 most valuable feature of H2O.ai is that it is plug-and-play."
"The ease of use in connecting to our cluster machines."
"AutoML helps in hands-free initial evaluations of efficiency/accuracy of ML algorithms."
"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."
"It is helpful, intuitive, and easy to use. The learning curve is not too steep."
"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."
"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."
"Quick deployment to dashboards and analytics features (using SAS Visual Statistics and Enterprise Guide). Easy to create a simple forecast and discover business insights using segmentation tools."
"It integrates well with SAS, making it simple and quick for developers."
"We've found the product to be stable and reliable."
"I use Visual Analytics for enterprise reporting."
"The technical support services are good."
"Data handling is one of the best features of SAS Visual Analytics."
"The product is stable, reliable, and scalable."
"It's quite easy to learn and to progress with SAS from an end-user perspective."
 

Cons

"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."
"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."
"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."
"H2O.ai can improve in areas like multimodal support and prompt engineering."
"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 visualization should be better in SAS Visual Analytics. It is easy to use but when compared to other solutions it is lacking and the support is not very good."
"SAS Visual Analytics could improve by making it more accessible for users outside the organization."
"The reason we haven't rolled it out across the board is due to the fact that the licensing is so expensive."
"The product is expensive and needs the integration of more languages."
"There are scalability issues. It depends on the data volume and number of end-users. VA requires a lot of hardware resources to move volumes of data."
"It is not as mature as competitors such as Tableau and QlikView."
"There are certain shortcomings in the tool's support services, making it an area where improvements are required."
"The solution should improve its graphics."
 

Pricing and Cost Advice

"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."
"The cost of the solution can be expensive. There is an additional cost for users."
"Licensing is simple."
"I work with the tool's free version...The tool's corporate version is very expensive and requires a monthly hire."
"SAS Visual Analytics is expensive, as is the rest of the platform."
"$10,000 per annum for an enterprise license."
"Visual Analytics is expensive for a small company like mine. You also need to deploy it on a server or cloud, so you pay for the license as well as the cost of the cloud or the server that you will deploy on."
"It's approximately $114,000 US dollars per year."
"It was licensed for corporate use, and its licensing was on a yearly basis."
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Top Industries

By visitors reading reviews
Financial Services Firm
16%
Computer Software Company
16%
Manufacturing Company
9%
Educational Organization
7%
Financial Services Firm
19%
Government
11%
Computer Software Company
10%
University
8%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

What needs improvement with H2O.ai?
H2O.ai can improve in areas like multimodal support and prompt engineering. They are already working on updates and changes. Although I haven't explored all the new products they've added to their ...
What is your primary use case for H2O.ai?
We mostly used the solution in the domain that I'm working. We had most of the use cases around chatbots and conversational BI.
What advice do you have for others considering H2O.ai?
It is important to address data privacy concerns and ensure you're choosing the right vendor that meets your use case demands. Also, you may leave my name, Kashif, but please keep the company name ...
What do you like most about SAS Visual Analytics?
The most solution's notable aspect, in my view, is the ability to integrate various data sources and harness advanced technologies such as machine learning and artificial intelligence. This helps w...
What is your experience regarding pricing and costs for SAS Visual Analytics?
It's about an average of five. It's easy to scale, but it comes with cost.
What needs improvement with SAS Visual Analytics?
In terms of configuration, I would like to see AI capabilities since many applications are now integrating AI. It may be that our current subscription does not include AI-enabled features, but I wo...
 

Also Known As

No data available
SAS BI
 

Overview

 

Sample Customers

poder.io, Stanley Black & Decker, G5, PWC, Comcast, Cisco
Staples, Ausgrid, Scotiabank, the Australian Institute of Health and Welfare, the Blue Cross and Blue Shield of North Carolina, Oklahoma Gas & Electric, Xcel Energy, and Triad Analytics Solutions.
Find out what your peers are saying about Databricks, Knime, Amazon Web Services (AWS) and others in Data Science Platforms. Updated: July 2025.
863,679 professionals have used our research since 2012.