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Amazon SageMaker 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:
 

ROI

Sentiment score
7.3
Amazon SageMaker offers significant ROI with cost reductions, time savings, and notable financial benefits, especially in fraud detection and targeted ads.
Sentiment score
6.0
SAS Visual Analytics offers advanced features but competes with lower-cost tools like Tableau for efficient data analysis and reporting.
The return on investment varies by use case and offers significant value in revenue increases and cost saving capabilities, especially in real time fraud detection and targeted advertisements.
 

Customer Service

Sentiment score
7.2
Amazon SageMaker customer service has mixed reviews, with satisfaction varying based on user experience, support promptness, and service tier.
Sentiment score
6.7
SAS Visual Analytics customer service has varied feedback, with generally good technical support but inconsistent experiences due to outsourcing.
The technical support from AWS is excellent.
The support is very good with well-trained engineers.
 

Scalability Issues

Sentiment score
7.6
Amazon SageMaker is highly scalable, handling diverse data needs effectively but may require deployment expertise for optimal efficiency.
Sentiment score
7.8
SAS Visual Analytics offers excellent scalability for large enterprises, though costs may be high for smaller organizations.
The availability of GPU instances can be a challenge, requiring proper planning.
Amazon SageMaker is scalable and works well from an infrastructure perspective.
 

Stability Issues

Sentiment score
7.8
Amazon SageMaker is stable and reliable, with minor issues mainly due to user configuration errors, not infrastructure problems.
Sentiment score
7.0
SAS Visual Analytics is generally stable, with some users noting occasional performance issues and others appreciating technical support.
I rate the stability of Amazon SageMaker between seven and eight.
 

Room For Improvement

Amazon SageMaker needs UI simplification, better documentation, cost efficiency improvements, enhanced security, scalability, training resources, and performance optimization.
SAS Visual Analytics is costly and complex, with integration issues and limited features, deterring smaller companies due to licensing costs.
Having all documentation easily accessible on the front page of SageMaker would be a great improvement.
Integration of the latest machine learning models like the new Amazon LLM models could enhance its capabilities.
This would empower citizen data scientists to utilize the tool more effectively since many data scientists do not have a core development background.
 

Setup Cost

Amazon SageMaker is costly, especially for notebook instances, with better visibility needed to optimize pay-as-you-go costs.
SAS Visual Analytics is costly for small businesses but valued by enterprises for self-service capabilities, despite complex licensing.
For a single user, prices might be high yet could be cheaper for user-managed services compared to AWS-managed services.
The pricing can be up to eight or nine out of ten, making it more expensive than some cloud alternatives yet more economical than on-premises setups.
The cost for small to medium instances is not very high.
 

Valuable Features

Amazon SageMaker provides flexible AI/ML solutions with easy AWS integration, strong deployment features, and comprehensive tools for scalability.
SAS Visual Analytics offers user-friendly data analysis, reporting, and visualization with robust AI integration and intuitive non-technical access.
These features facilitate rapid development and deployment of AI applications.
SageMaker supports building, training, and deploying AI models from scratch, which is crucial for my ML project.
This allows monitoring and performance grading, as I instantly know when someone has a bad call.
 

Categories and Ranking

Amazon SageMaker
Average Rating
7.8
Reviews Sentiment
7.1
Number of Reviews
36
Ranking in other categories
Data Science Platforms (3rd), AI Development Platforms (4th)
SAS Visual Analytics
Average Rating
8.2
Reviews Sentiment
6.9
Number of Reviews
39
Ranking in other categories
Data Visualization (8th)
 

Mindshare comparison

While both are Business Intelligence solutions, they serve different purposes. Amazon SageMaker is designed for Data Science Platforms and holds a mindshare of 7.6%, down 9.9% compared to last year.
SAS Visual Analytics, on the other hand, focuses on Data Visualization, holds 4.7% mindshare, down 6.5% since last year.
Data Science Platforms
Data Visualization
 

Featured Reviews

Hemant Paralkar - PeerSpot reviewer
Improves team collaboration with advanced feature sharing but needs a better user experience
Improvement is needed in the no-code and low-code capabilities of Amazon SageMaker. This would empower citizen data scientists to utilize the tool more effectively since many data scientists do not have a core development background. Additionally, dealing with frequent UI updates can be challenging, especially for infrastructure architects like myself. It involves effort to migrate to new UIs, making the updates not seamless. User auditing requires enhancements as tracking operations performed by users can be difficult due to dynamic IP validation and role propagation.
Robert Heck - PeerSpot reviewer
A great solution for big organizations, complex business requirements, and highly sophisticated and specialized statistics
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. For instance the system does not come with a pre-defined accounting, budgeting or planning model for a particular industry. Some competitors come with such a model (e.g. for retail companies) which makes the implementation of course easier if the customer can comproise with this predefined model. SAS does not provide such models but does not demand customers to comply with a foreign business model.
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Top Industries

By visitors reading reviews
Financial Services Firm
18%
Educational Organization
15%
Computer Software Company
11%
Manufacturing Company
9%
Financial Services Firm
20%
Government
13%
Computer Software Company
10%
University
8%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

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 designed to accelerate innovation projects. It is based on Spark so it is very fast. It...
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 ChatGPT that requires models. We utilize Amazon SageMaker to create endpoints for t...
What is your experience regarding pricing and costs for Amazon SageMaker?
Before deploying SageMaker, I reviewed the pricing, especially for notebook instances. The cost for small to medium instances is not very high.
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?
Some capabilities are missing compared to Power BI, especially when working with spreadsheet types. Furthermore, Excel is more customizable compared to SAS Visual Analytics, which can be quite rigi...
 

Also Known As

AWS SageMaker, SageMaker
SAS BI
 

Overview

 

Sample Customers

DigitalGlobe, Thomson Reuters Center for AI and Cognitive Computing, Hotels.com, GE Healthcare, Tinder, Intuit
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: January 2025.
832,138 professionals have used our research since 2012.