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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
6.8
Number of Reviews
10
Ranking in other categories
Data Science Platforms (18th), Model Monitoring (4th)
SAS Visual Analytics
Average Rating
8.2
Reviews Sentiment
7.0
Number of Reviews
40
Ranking in other categories
Data Visualization (9th)
 

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 1.5% compared to last year.
SAS Visual Analytics, on the other hand, focuses on Data Visualization, holds 3.4% mindshare, down 5.2% since last year.
Data Science Platforms Market Share Distribution
ProductMarket Share (%)
H2O.ai1.7%
Databricks13.9%
KNIME Business Hub11.9%
Other72.5%
Data Science Platforms
Data Visualization Market Share Distribution
ProductMarket Share (%)
SAS Visual Analytics3.4%
Tableau Enterprise19.2%
Apache Superset9.2%
Other68.2%
Data Visualization
 

Featured Reviews

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.
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 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."
"AutoML helps in hands-free initial evaluations of efficiency/accuracy of ML algorithms."
"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."
"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."
"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."
"I believe that the possibilities for exploring data and formulating visual results are quite good because it allows the business analyst to have different perspectives on the data."
"We've found the product to be stable and reliable."
"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."
"I like SAS Visual Analytics for its ability to provide an initial understanding of data through exploration, even before deep analytics."
"The alert generation feature also helps in sending out ad hoc messages to the business users if business thresholds have been crossed."
"The technical support services are good."
"Visual Analytics is very easy to use. I use Visual Analytics for all the typical use cases except text mining. I used it to analyze data and monitor statistics, not text mining. I also use it for data visualization as well as creating interactive dashboards and infographics."
"The speed to display charts and react to users' choices is great."
 

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."
"On the topic of model training and model governance, this solution cannot handle ten or twelve models running at the same time."
"H2O.ai can improve in areas like multimodal support and prompt engineering."
"The model management features could be improved."
"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."
"I would like to see more features related to deployment."
"It lacks the data manipulation capabilities of R and Pandas DataFrames. We would kill for dplyr offloading H2O."
"Referring to bullet-3 as well, H2O DataFrame manipulation capabilities are too primitive."
"The solution is a little weak at the front end."
"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."
"The reason we haven't rolled it out across the board is due to the fact that the licensing is so expensive."
"SAS Visual Analytics could improve by making it more accessible for users outside the organization."
"In Brazil, there are few documents, courses, and other resources for studying and implementing the tool."
"The solution should improve its graphics."
"SAS Visual Analytics could be more user-friendly."
"There is a need for coding when it comes to digital reporting which can be intimidating."
 

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 product is expensive."
"SAS Visual Analytics is expensive, as is the rest of the platform."
"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."
"Licensing is simple."
"It was licensed for corporate use, and its licensing was on a yearly basis."
"It's approximately $114,000 US dollars per year."
"I work with the tool's free version...The tool's corporate version is very expensive and requires a monthly hire."
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Top Industries

By visitors reading reviews
Financial Services Firm
16%
Computer Software Company
15%
Manufacturing Company
8%
Educational Organization
7%
Financial Services Firm
19%
Government
10%
Computer Software Company
9%
University
8%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business2
Midsize Enterprise3
Large Enterprise7
By reviewers
Company SizeCount
Small Business13
Midsize Enterprise8
Large Enterprise19
 

Questions from the Community

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...
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, Amazon Web Services (AWS), Knime and others in Data Science Platforms. Updated: September 2025.
869,832 professionals have used our research since 2012.