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Amazon SageMaker vs SAS Enterprise Miner comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 5, 2024

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

Amazon SageMaker
Ranking in Data Science Platforms
2nd
Average Rating
7.8
Reviews Sentiment
7.0
Number of Reviews
38
Ranking in other categories
AI Development Platforms (4th)
SAS Enterprise Miner
Ranking in Data Science Platforms
24th
Average Rating
7.6
Reviews Sentiment
6.2
Number of Reviews
13
Ranking in other categories
Data Mining (7th)
 

Mindshare comparison

As of February 2026, in the Data Science Platforms category, the mindshare of Amazon SageMaker is 4.3%, down from 7.6% compared to the previous year. The mindshare of SAS Enterprise Miner is 1.6%, up from 0.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Science Platforms Market Share Distribution
ProductMarket Share (%)
Amazon SageMaker4.3%
SAS Enterprise Miner1.6%
Other94.1%
Data Science Platforms
 

Featured Reviews

Saurabh Jaiswal - PeerSpot reviewer
Python AWS & AI Expert at a tech consulting company
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.
reviewer1352853 - PeerSpot reviewer
Executive Head of analytics at a retailer with 5,001-10,000 employees
A stable product that is easy to deploy and can be used for structured and unstructured data mining
We use the solution for predictive analytics to do structured and unstructured data mining I like the way the product visually shows the data pipeline. The product must provide better integration with cloud-native technologies. I have been using the solution for 20 years. The product is very…

Quotes from Members

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

Pros

"The tool makes our ML model development a bit more efficient because everything is in one environment."
"The product aggregates everything we need to build and deploy machine learning models in one place."
"The most valuable feature of Amazon SageMaker for me is the model deployment service."
"The tangible benefits we have observed from using Amazon SageMaker include improved time to insight and generally the common stack that is easier to support over time."
"Feature Store, CodeCommit, versioning, model control, and CI/CD pipelines are the most valuable features in Amazon SageMaker."
"The feature I found most valuable is the data catalog, as it assists with the lineage of data through the preparation pipeline."
"The solution's ability to improve work at my organization stems from the ensemble model and a combination of various models it provides."
"It's user-friendly for business teams as they can understand many aspects through the AWS interface."
"he solution is scalable."
"Good data management and analytics."
"The solution is very good for data mining or any mining issues."
"The most valuable feature is the decision tree creation."
"The technical support is very good."
"The setup is straightforward. Deployment doesn't take more than 30 minutes."
"Most of the features, especially on the data analysis tool pack, are really good. The way they do clustering and output is great. You can do fairly elaborate outputs. The results, the ensembles, all of these, are fantastic."
"The solution is able to handle quite large amounts of data beautifully."
 

Cons

"The payment and monitoring metrics are a bit confusing not only for Amazon SageMaker but also for the range of other products that fall under AWS, especially for a new user of the product."
"The documentation must be made clearer and more user-friendly."
"There is room for improvement in the collaboration with serverless architecture, particularly integration with AWS Lambda."
"The solution needs to be cheaper since it now charges per document for extraction."
"Having all documentation easily accessible on the front page of SageMaker would be a great improvement."
"Lacking in some machine learning pipelines."
"The product must provide better documentation."
"Improvement is needed in the no-code and low-code capabilities of Amazon SageMaker."
"The product must provide better integration with cloud-native technologies."
"Technical support could be improved."
"The solution needs an easier interface for the user. The user experience isn't so easy for our clients."
"The visualization of the models is not very attractive, so the graphics should be improved."
"The initial setup is challenging if doing it for the first time."
"The user interface of the solution needs improvement. It needs to be more visual."
"The solution is much more complex than other options."
"The ease of use can be improved. When you are new it seems a bit complex."
 

Pricing and Cost Advice

"You don't pay for Sagemaker. You only pay for the compute instances in your storage."
"On average, customers pay about $300,000 USD per month."
"The support costs are 10% of the Amazon fees and it comes by default."
"In terms of pricing, I'd also rate it ten out of ten because it's been beneficial compared to other solutions."
"Databricks solution is less costly than Amazon SageMaker."
"On a scale from one to ten, where one is cheap, and ten is expensive, I rate the solution's pricing a six out of ten."
"The pricing is complicated as it is based on what kind of machines you are using, the type of storage, and the kind of computation."
"SageMaker is worth the money for our use case."
"The solution is expensive for an individual, but for an enterprise/institution (purchasing bulk licenses), it is not a high price for the use that will come from it."
"The solution must improve its licensing models."
"This solution is for large corporations because not everybody can afford it."
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Top Industries

By visitors reading reviews
Financial Services Firm
18%
Computer Software Company
10%
Manufacturing Company
9%
University
6%
Financial Services Firm
24%
Educational Organization
11%
Manufacturing Company
10%
University
8%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business12
Midsize Enterprise11
Large Enterprise17
By reviewers
Company SizeCount
Small Business3
Midsize Enterprise4
Large Enterprise7
 

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?
If you manage it effectively, their pricing is reasonable. It's similar to anything in the cloud; if you don't manage it properly, it can be expensive, but if you do, it's fine.
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Also Known As

AWS SageMaker, SageMaker
Enterprise Miner
 

Overview

 

Sample Customers

DigitalGlobe, Thomson Reuters Center for AI and Cognitive Computing, Hotels.com, GE Healthcare, Tinder, Intuit
Generali Hellas, Gitanjali Group, Gloucestershire Constabulary, GS Home Shopping, HealthPartners, IAG New Zealand, iJET, Invacare
Find out what your peers are saying about Amazon SageMaker vs. SAS Enterprise Miner and other solutions. Updated: December 2025.
881,360 professionals have used our research since 2012.