Amazon SageMaker vs Saturn Cloud comparison

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Amazon SageMaker

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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: December 2024).

Amazon SageMaker vs. Saturn Cloud

December 2024

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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 (4th)

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 (14th)

Mindshare comparison

As of January 2025, in the Data Science Platforms category, the mindshare of Amazon SageMaker is 7.7%, down from 10.0% 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

Hemant Paralkar

Lead Consultant at Saama

Improves team collaboration with advanced feature sharing but needs a better user experience

Improvement is needed in the no-code and low-code capabilities of Amazon SageMaker. This would empower citizen data scientists to utilize the tool more effectively since many data scientists do not have a core development background. Additionally, dealing with frequent UI updates can be challenging, especially for infrastructure architects like myself. It involves effort to migrate to new UIs, making the updates not seamless. User auditing requires enhancements as tracking operations performed by users can be difficult due to dynamic IP validation and role propagation.

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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.

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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."

"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 these models, making accessing them convenient as needed."

"They offer insights into everyone making calls in my organization."

"The few projects we have done have been promising."

"The intuitive interface and streamlined user experience make it easy to navigate and set up various tools like Visual Studio Code or Jupyter Notebook."

"The most valuable features include the ML operations that allow for designing, deploying, testing, and evaluating models."

"Allows you to create API endpoints."

"The tool makes our ML model development a bit more efficient because everything is in one environment."

More Amazon SageMaker pros

"They provide a centralized space for data, code, and results."

"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."

"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."

"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."

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."

"Improvement is needed in the no-code and low-code capabilities of Amazon SageMaker. This would empower citizen data scientists to utilize the tool more effectively since many data scientists do not have a core development background."

"Creating notebook instances for multiple users is pretty expensive in Amazon SageMaker."

"Amazon SageMaker could improve in the area of hyperparameter tuning by offering more automated suggestions and tips during the tuning process."

"SageMaker would be improved with the addition of reporting services."

"Amazon might need to emphasize its capabilities in generative models more effectively."

"The model repository is a concern as models are stored on a bucket and there's an issue with versioning."

"In general, improvements are needed on the performance side of the product's graphical user interface-related area since it consumes a lot of time for a user."

More Amazon SageMaker cons

"Providing more detailed and beginner-friendly documentation, especially for advanced features, could greatly enhance the user experience."

"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."

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

"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."

"It would be nice to have more hardware category options, like TPU coprocessors or ARM64 CPUs."

Pricing and Cost Advice

"The pricing could be better, especially for querying. The per-query model feels expensive."

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

"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."

"I would rate the solution's price a ten out of ten since it is very high."

"The tool's pricing is reasonable."

"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."

"The solution is relatively cheaper."

More Amazon SageMaker pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

18%

Educational Organization

14%

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?

Before deploying SageMaker, I reviewed the pricing, especially for notebook instances. The cost for small to medium instances is not very high.

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 ...

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Product Reports

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Amazon SageMaker

January 2025

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December 2024

Also Known As

AWS SageMaker, SageMaker

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Amazon Web Services (AWS)

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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.

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.