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Amazon SageMaker vs Saturn Cloud 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)
Saturn Cloud
Ranking in Data Science Platforms
16th
Average Rating
10.0
Reviews Sentiment
7.5
Number of Reviews
6
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of March 2026, in the Data Science Platforms category, the mindshare of Amazon SageMaker is 4.0%, down from 7.5% compared to the previous year. The mindshare of Saturn Cloud is 1.0%, up from 0.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Science Platforms Mindshare Distribution
ProductMindshare (%)
Amazon SageMaker4.0%
Saturn Cloud1.0%
Other95.0%
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.
Filip Stefanovski - PeerSpot reviewer
Works at a tech consulting company with 51-200 employees
Easy to use with good performance and collaborative features
My main suggestion for improvement centers on pricing. Introducing a tier modelled after AWS spot instances would be a game-changer. Users could bid on unused compute capacity, potentially leading to significant cost savings during off-peak hours or for less time-critical tasks. Spot instances empower users with tighter budgets or fluctuating workloads to strategically leverage lower-cost resources for development, experimentation, and background tasks. This frees up on-demand instances for truly time-sensitive work.

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 features in Amazon SageMaker are its AutoML, feature store, and automated hyperparameter tuning capabilities."
"The tool has made client management easier where patients need to upload their health records and we can use the tool to understand details on treatment date, amount, etc."
"They are doing a good job of evolving."
"The evolution from SageMaker Classic to SageMaker Studio, particularly the UI part of Studio, is commendable."
"They offer insights into everyone making calls in my organization."
"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."
"It's user-friendly for business teams as they can understand many aspects through the AWS interface."
"We've had no problems with SageMaker's stability."
"There is plenty of computational resources (both GPU, CPU and disk space)."
"The feature I like the most about Saturn Cloud is that it has lightning-fast CPUs."
"It offered an excellent development environment while not touching our production cloud resources."
"They provide a centralized space for data, code, and results."
"Saturn Cloud supports GPU as part of the environment, which is essential for many computational tasks in machine learning projects. It also allows us to edit the environment, including the image, before we start the cloud resources. This feature lets us quickly set up the environment without the hassle of moving the data and code to another cloud device."
"It didn't take long to see that Saturn Cloud could scale with my needs, providing more resources when required."
 

Cons

"In my opinion, one improvement for Amazon SageMaker would be to offer serverless GPUs. Currently, we incur costs on an hourly basis. It would be beneficial if the tool could provide pay-as-you-go pricing based on endpoints."
"There are other better solutions for large data, such as Databricks."
"The dashboard could be improved by including more features and providing more information about deployed models, their drift, performance, scaling, and customization options."
"The documentation must be made clearer and more user-friendly."
"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."
"SageMaker would be improved with the addition of reporting services."
"One area for improvement is the pricing, which can be quite high."
"The training modules could be enhanced. We had to take in-person training to fully understand SageMaker, and while the trainers were great, I think more comprehensive online modules would be helpful."
"My main suggestion for improvement centers on pricing. Introducing a tier modelled after AWS spot instances would be a game-changer."
"Public Clouds integration and sandbox environments would be a true game changer."
"We'd like to have the capability for installing more libraries."
"Providing more detailed and beginner-friendly documentation, especially for advanced features, could greatly enhance the user experience."
"It would be nice to have more hardware category options, like TPU coprocessors or ARM64 CPUs."
"Saturn Cloud should include prebuilt images for advanced data science packages like LightGBM in the next release. If possible, they should also provide a Kaggle image, which contains the most common Python packages used in machine learning."
 

Pricing and Cost Advice

"There is no license required for the solution since you can use it on demand."
"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."
"Amazon SageMaker is a very expensive product."
"The pricing is comparable."
"The tool's pricing is reasonable."
"I would rate the solution's price a ten out of ten since it is very high."
"On average, customers pay about $300,000 USD per month."
"The pricing could be better, especially for querying. The per-query model feels expensive."
Information not available
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Top Industries

By visitors reading reviews
Financial Services Firm
17%
Manufacturing Company
9%
Computer Software Company
9%
University
6%
No data available
 

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 Business4
Midsize Enterprise1
Large Enterprise3
 

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.
What do you like most about Saturn Cloud?
There is plenty of computational resources (both GPU, CPU and disk space).
What needs improvement with Saturn Cloud?
My main suggestion for improvement centers on pricing. Introducing a tier modelled after AWS spot instances would be a game-changer. Users could bid on unused compute capacity, potentially leading ...
What is your primary use case for Saturn Cloud?
I'm leveraging a cloud-based platform for competitive machine learning. Tight deadlines and resource-intensive models demand powerful hardware. The cloud provides scalable GPUs and RAM, letting me ...
 

Also Known As

AWS SageMaker, SageMaker
No data available
 

Overview

 

Sample Customers

DigitalGlobe, Thomson Reuters Center for AI and Cognitive Computing, Hotels.com, GE Healthcare, Tinder, Intuit
Nvidia, Snowflake, Kaggle, Faeth, Advantest, Stanford University, Senseye and more.
Find out what your peers are saying about Amazon SageMaker vs. Saturn Cloud and other solutions. Updated: March 2026.
884,873 professionals have used our research since 2012.