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: April 2025).

Amazon SageMaker vs. Saturn Cloud

April 2025

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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

2nd

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

9th

Average Rating

10.0

Reviews Sentiment

7.5

Number of Reviews

Ranking in other categories

AWS Marketplace (17th)

Mindshare comparison

As of June 2025, in the Data Science Platforms category, the mindshare of Amazon SageMaker is 6.5%, down from 9.6% 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.

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Filip Stefanovski

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.

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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 support is very good with well-trained engineers whose training curriculum is rigorous."

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

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

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

"Amazon SageMaker is highly valuable for managing ML workloads. It connects to AWS cloud resources, making it easy to deploy algorithms and collaborate using tools like GitLab. It offers a wide range of Python libraries and other necessary tools for modelling and algorithms."

"The deployment is very good, where you only need to press a few buttons."

"They are doing a good job of evolving."

"The solution is easy to scale...The documentation and online community support have been sufficient for us so far."

More Amazon SageMaker pros

"The feature I like the most about Saturn Cloud is that it has lightning-fast CPUs."

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

"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."

"It offered an excellent development environment while not touching our production cloud resources."

Cons

"One area where Amazon SageMaker could improve is its pricing. The high costs can drive companies to explore other cloud options. Additionally, while generally good, the updates sometimes come with bugs, and the documentation could be much better. More examples and clearer guidance would be helpful."

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

"Improvement is needed in the no-code and low-code capabilities of Amazon SageMaker."

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

"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."

"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."

"The pricing of the solution is an issue...In SageMaker, monitoring could be improved by supporting more data types other than JSON and CSV."

"The solution is complex to use."

More Amazon SageMaker cons

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

"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."

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

"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

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

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

"The product is expensive."

"Databricks solution is less costly than Amazon SageMaker."

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

"On average, customers pay about $300,000 USD per month."

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

"You don't pay for Sagemaker. You only pay for the compute instances in your storage."

More Amazon SageMaker pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

18%

Computer Software Company

12%

Educational Organization

Manufacturing Company

No data available

Company Size

By reviewers

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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?

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

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Product Reports

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Amazon SageMaker

May 2025

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May 2025

Also Known As

AWS SageMaker, SageMaker

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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.

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.