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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
23rd
Average Rating
7.6
Reviews Sentiment
6.2
Number of Reviews
13
Ranking in other categories
Data Mining (7th)
 

Mindshare comparison

As of January 2026, in the Data Science Platforms category, the mindshare of Amazon SageMaker is 4.6%, 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 SageMaker4.6%
SAS Enterprise Miner1.6%
Other93.8%
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 product aggregates everything we need to build and deploy machine learning models in one place."
"The technical support of the tool was good."
"The most tool's valuable feature, in my experience, is hyperparameter tuning. It allows us to test different parameters for the same model in parallel, which helps us quickly identify the configuration that yields the highest accuracy. This parallel computing capability saves us a lot of time."
"I have contacted the solution's technical support, and they were really good. I rate the technical support a ten out of ten."
"The feature I found most valuable is the data catalog, as it assists with the lineage of data through the preparation pipeline."
"I recommend SageMaker for ML projects if you need to build models from scratch."
"The Autopilot feature is really good because it's helpful for people who don't have much experience with coding or data pipelines. When we suggest SageMaker to clients, they don't have to go through all the steps manually. They can leverage Autopilot to choose variables, run experiments, and monitor costs. The results are also pretty accurate."
"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."
"I like the way the product visually shows the data pipeline."
"The technical support is very good."
"The most valuable feature is the decision tree creation."
"The setup is straightforward. Deployment doesn't take more than 30 minutes."
"I found the ease of use of the solution the most valuable. Additionally, other valuable features include: the user interface, power to extract data, compatibility with other technologies (specifically with PS400), and automation of several tasks."
"Good data management and analytics."
"The solution is very good for data mining or any mining issues."
"he solution is scalable."
 

Cons

"While integration is available, there are concerns about how secure this integration is, particularly when exposing data to SageMaker."
"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."
"Amazon might need to emphasize its capabilities in generative models more effectively."
"The documentation must be made clearer and more user-friendly."
"The main challenge with Amazon SageMaker is the integrations."
"Improvement is needed in the no-code and low-code capabilities of Amazon SageMaker."
"The model repository is a concern as models are stored on a bucket and there's an issue with versioning."
"The product must provide better integration with cloud-native technologies."
"The ease of use can be improved. When you are new it seems a bit complex."
"Virtualization could be much better."
"The user interface of the solution needs improvement. It needs to be more visual."
"The initial setup is challenging if doing it for the first time."
"The solution needs an easier interface for the user. The user experience isn't so easy for our clients."
"The solution is very stable, but we do have some problems with discrepancies involving SAS not matching with the latest Java versions. It's not stable in cases where SAS tries to run on a different version because SAS doesn't connect with the latest Java update. Once a month we need to restart systems from scratch."
"The solution is much more complex than other options."
 

Pricing and Cost Advice

"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."
"In terms of pricing, I'd also rate it ten out of ten because it's been beneficial compared to other solutions."
"I rate the pricing a five on a scale of one to ten, where one is the lowest price, and ten is the highest price. The solution is priced reasonably. There is no additional cost to be paid in excess of the standard licensing fees."
"On average, customers pay about $300,000 USD per month."
"The pricing is comparable."
"There is no license required for the solution since you can use it on demand."
"The pricing could be better, especially for querying. The per-query model feels expensive."
"I would rate the solution's price a ten out of ten since it is very high."
"The solution must improve its licensing models."
"This solution is for large corporations because not everybody can afford it."
"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."
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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
25%
Educational Organization
11%
Manufacturing Company
10%
University
9%
 

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.
881,114 professionals have used our research since 2012.