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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
8
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 1.7%, up 1.4% compared to last year.
SAS Visual Analytics, on the other hand, focuses on Data Visualization, holds 4.0% mindshare, down 6.0% 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…
Renato Vazamin - PeerSpot reviewer
Single environment for multiple phases saves us time, and has good visualizations
We had that solution installed previously in another solution, Selvaya, but I don't think we used it at the time. We are now using SAS Detect Investigation as a complementary solution, in which we have part of the process, use a gene, SAS collects information and identifies some business situations, and the business guys use Visual Analytics to explore the results of the process. We previously used the FICO platform, but we switched because FICO's pricing was not scalable. Bringing more data or workloads to the platform required a significant investment in order to scale. We needed to change because we have a lot of data to process every day. FICO was also a little more complicated than SAS Visual Analytics.

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 H2O.ai is that it is plug-and-play."
"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."
"AutoML helps in hands-free initial evaluations of efficiency/accuracy of ML algorithms."
"The ease of use in connecting to our cluster machines."
"It is helpful, intuitive, and easy to use. The learning curve is not too steep."
"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 features are the machine learning tools, the support for Jupyter Notebooks, and the collaboration that allows you to share it across people."
"The alert generation feature also helps in sending out ad hoc messages to the business users if business thresholds have been crossed."
"The visualization capabilities and the email functionality are most beneficial."
"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."
"The flexibility of the configuration is valuable to me."
"We've found the product to be stable and reliable."
"It's a stable, reliable product."
"It's quite easy to learn and to progress with SAS from an end-user perspective."
"The tool's most valuable features are its ease of use and advanced data visualization capabilities."
 

Cons

"On the topic of model training and model governance, this solution cannot handle ten or twelve models running at the same time."
"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."
"H2O.ai can improve in areas like multimodal support and prompt engineering."
"The model management features could be improved."
"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."
"I would like to see more features related to deployment."
"The integration aspects of the solution could be improved."
"The installation process can be a bit complex."
"Colours used on report objects"
"The licensing ends up being more expensive than other options."
"There are a few little things that are predefined and can be done out of the box immediately. There is no business intelligence application that is predefined, which is something some customers or prospects would love to have. Small and mid-sized companies would struggle with it because they prefer something standard that has been predefined by somebody else."
"A bit more flexibility in the temperatization will be helpful."
"In Brazil, there are few documents, courses, and other resources for studying and implementing the tool."
"SAS Visual Analytics could be more user-friendly."
 

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."
"It's approximately $114,000 US dollars per year."
"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."
"$10,000 per annum for an enterprise license."
"The product is expensive."
"It was licensed for corporate use, and its licensing was on a yearly basis."
"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."
"Licensing is simple."
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Top Industries

By visitors reading reviews
Financial Services Firm
18%
Computer Software Company
13%
Manufacturing Company
10%
Educational Organization
6%
Financial Services Firm
21%
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...
 

Comparisons

 

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, Amazon Web Services (AWS), Knime and others in Data Science Platforms. Updated: May 2025.
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