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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
17th
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 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 Saturn Cloud is 0.7%, up from 0.2% 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%
Saturn Cloud0.7%
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.
Alessandro Trinca Tornidor - PeerSpot reviewer
Sviluppatore software at TeamSystem
Good for creating POCs, training machine learning models, and experimenting without local resources
The project I’m currently working on relies on CUDA, but my local PC does not have any Nvidia GPUs. I’ve found the computational resources and ease of use provided by Saturn Cloud invaluable. Also, there are many ready-to-use Docker images and a rich documentation portal with useful examples. The dashboard for creating a new virtual environment contains almost all the features I needed: environment variable definitions, git repositories cloning directly from the new resources page, and an edit field to define a custom script during the boot process. For this reason, Saturn Cloud.io is a very good solution for creating POCs, training machine learning models, and generally experimenting a bit without worrying about local resources.

Quotes from Members

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

Pros

"The technical support from AWS is excellent."
"I have contacted the solution's technical support, and they were really good. I rate the technical support a ten out of ten."
"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."
"The intuitive interface and streamlined user experience make it easy to navigate and set up various tools like Visual Studio Code or Jupyter Notebook."
"SageMaker is a comprehensive platform where I can perform all machine learning activities."
"The feature I found most valuable is the data catalog, as it assists with the lineage of data through the preparation pipeline."
"It's user-friendly for business teams as they can understand many aspects through the AWS interface."
"The most valuable feature of Amazon SageMaker is SageMaker Studio."
"The feature I like the most about Saturn Cloud is that it has lightning-fast CPUs."
"They provide a centralized space for data, code, and results."
"There is plenty of computational resources (both GPU, CPU and disk space)."
"It offered an excellent development environment while not touching our production cloud resources."
"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

"I had to create custom templates for labeling multi-data sets, such as text and images, which was time-consuming."
"Amazon might need to emphasize its capabilities in generative models more effectively."
"Improvement is needed in the no-code and low-code capabilities of Amazon SageMaker."
"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."
"There are other better solutions for large data, such as Databricks."
"One area for improvement is the pricing, which can be quite high."
"For any cloud provider, the cost has to be substantially reduced, especially in the case of Amazon SageMaker, which is extremely expensive for huge workloads."
"The solution requires a lot of data to train the model."
"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."
"Providing more detailed and beginner-friendly documentation, especially for advanced features, could greatly enhance the user experience."
"We'd like to have the capability for installing more libraries."
"My main suggestion for improvement centers on pricing. Introducing a tier modelled after AWS spot instances would be a game-changer."
"It would be nice to have more hardware category options, like TPU coprocessors or ARM64 CPUs."
"Public Clouds integration and sandbox environments would be a true game changer."
 

Pricing and Cost Advice

"There is no license required for the solution since you can use it on demand."
"The pricing is comparable."
"The product is expensive."
"SageMaker is worth the money for our use case."
"You don't pay for Sagemaker. You only pay for the compute instances in your storage."
"In terms of pricing, I'd also rate it ten out of ten because it's been beneficial compared to other solutions."
"The cost offers a pay-as-you-go pricing model. It depends on the instance that you do."
"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
18%
Computer Software Company
10%
Manufacturing 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: December 2025.
881,707 professionals have used our research since 2012.