Amazon SageMaker vs Saturn Cloud comparison

Read 6 Saturn Cloud reviews

159 Views
76 Comparison Views

100% willing to recommend

Amazon SageMaker

Comparison Buyer's Guide

Download the report

Executive SummaryUpdated on Dec 5, 2024

Amazon SageMaker and Saturn Cloud are competing cloud-based machine learning platforms. Saturn Cloud appears to hold an edge in feature richness, while Amazon SageMaker stands out with competitive pricing and integration within the AWS ecosystem.

Features: Amazon SageMaker offers integrated tools for building, training, and deploying models, support for diverse frameworks, and seamless AWS integration. Saturn Cloud provides an adaptable high-performance environment, lightning-fast computational resources, and collaborative features, making it ideal for data science teams.

Room for Improvement: Amazon SageMaker could enhance its feature set beyond AWS integration and improve its initial setup complexity. Saturn Cloud might benefit from expanding its predefined environments, streamlining advanced configuration processes, and reducing initial setup costs.

Ease of Deployment and Customer Service: Amazon SageMaker facilitates deployment through AWS integration but may rely heavily on AWS tools. Its customer service is comprehensive within this ecosystem. Saturn Cloud offers a quick setup with minimal configuration and responsive customer service, allowing for rapid scaling.

Pricing and ROI: Amazon SageMaker offers flexible pricing models that suit scalable business needs, making it cost-efficient over time. Saturn Cloud, despite higher initial costs, offers strong ROI through productivity gains and resource optimization for data-intensive businesses.

To learn more, read our detailed Amazon SageMaker vs. Saturn Cloud Report (Updated: April 2025).

Amazon SageMaker vs. Saturn Cloud

April 2025

Download the complete report

Helped 851,604 peers since 2012

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

3rd

Average Rating

7.8

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

AI Development Platforms (5th)

Saturn Cloud

Ranking in Data Science Platforms

10th

Average Rating

10.0

Reviews Sentiment

7.5

Number of Reviews

Ranking in other categories

AWS Marketplace (15th)

Mindshare comparison

As of May 2025, in the Data Science Platforms category, the mindshare of Amazon SageMaker is 6.9%, down from 9.7% compared to the previous year. The mindshare of Saturn Cloud is 0.2%, up from 0.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms

Featured Reviews

Saurabh Jaiswal

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 ( /products/amazon-sagemaker-reviews ), such as Firehose for connecting to data pipelines, are simple to use. Tools like AWS Glue ( /products/aws-glue-reviews ) integrate well for data transformations. The Databricks ( /products/databricks-reviews ) integration aids data scientists and engineers. SageMaker is fully managed, offers high availability, flexibility with TensorFlow ( /products/tensorflow-reviews ), PyTorch ( /products/pytorch-reviews ), and MXNet ( /products/mxnet-reviews ), and comes with pre-trained algorithms for forecasting, anomaly detection, and more.

Read full review

Alessandro Trinca Tornidor

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.

Read full review

Quotes from Members

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

Pros

"We've had no problems with SageMaker's stability."

"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 most valuable feature of Amazon SageMaker is that you don't have to do any programming in order to perform some of your use cases."

"The various integration options available in Amazon SageMaker, such as Firehose for connecting to data pipelines, are simple to use."

"One of the most valuable features of Amazon SageMaker for me is the one-touch deployment, which simplifies the process greatly."

"They are doing a good job of evolving."

"Feature Store, CodeCommit, versioning, model control, and CI/CD pipelines are the most valuable features in Amazon SageMaker."

"SageMaker offers functionalities like Jupyter Notebooks for development, built-in algorithms, model tuning, and options to deploy models on managed infrastructure."

More Amazon SageMaker pros

"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."

"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."

"There is plenty of computational resources (both GPU, CPU and disk space)."

Cons

"The product must provide better documentation."

"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."

"AI is a new area and AWS needs to have an internship training program available."

"The documentation must be made clearer and more user-friendly."

"I would suggest that Amazon SageMaker provide free slots to allow customers to practice, such as a free slot to try out working with a Sandbox."

"Lacking in some machine learning pipelines."

"The solution needs to be cheaper since it now charges per document for extraction."

"There are other better solutions for large data, such as Databricks."

More Amazon SageMaker cons

"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."

"My main suggestion for improvement centers on pricing. Introducing a tier modelled after AWS spot instances would be a 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."

"Public Clouds integration and sandbox environments would be a true game changer."

Pricing and Cost Advice

"The cost offers a pay-as-you-go pricing model. It depends on the instance that you do."

"There is no license required for the solution since you can use it on demand."

"SageMaker is worth the money for our use case."

"Databricks solution is less costly than Amazon SageMaker."

"The pricing is comparable."

"In terms of pricing, I'd also rate it ten out of ten because it's been beneficial compared to other solutions."

"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."

"The support costs are 10% of the Amazon fees and it comes by default."

More Amazon SageMaker pricing and cost advice

Information not available

See which vendors are best for you

Use our free recommendation engine to learn which Data Science Platforms solutions are best for your needs.

See recommendations

851,604 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

18%

Educational Organization

11%

Computer Software Company

11%

Manufacturing Company

No data available

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?

The pricing is high, around an eight. However, SageMaker offers free trials for the first two months, allowing users to determine which features they need. It is considered value for money given it...

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 ...

Databricks vs Amazon SageMaker

Comparisons

Compared 15% of the time

Azure OpenAI vs Amazon SageMaker

Compared 12% of the time

Dataiku vs Amazon SageMaker

Compared 11% of the time

Google Vertex AI vs Amazon SageMaker

Compared 8% of the time

Domino Data Science Platform vs Amazon SageMaker

Compared 7% of the time

More Amazon SageMaker Competitors

No data available

Product Reports

Download Amazon SageMaker product report

Amazon SageMaker

May 2025

Download Saturn Cloud product report

April 2025

Also Known As

AWS SageMaker, SageMaker

No data available

Overview

Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning.

Amazon Web Services (AWS)

Saturn Cloud is a cloud-based data science and machine learning platform that provides a scalable, flexible, and easy-to-use environment for data scientists and machine learning engineers. Saturn Cloud offers a variety of features and tools for data science, including: Compute resources (including CPUs, GPUs, and Dask clusters), Storage (object, block, and ephemeral storage), Networking, a variety of integrations with ML tools such as JupyterLab, RStudio, and TensorFlow.

Saturn Cloud is a good choice for data scientists and machine learning engineers who need a scalable, flexible, and easy-to-use environment.

Saturn Cloud also makes it easy to collaborate with other data scientists and machine learning engineers. You can share projects, notebooks, and data with others, and you can track changes to your work.

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.