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: July 2025).

Amazon SageMaker vs. Saturn Cloud

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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

7.0

Number of Reviews

Ranking in other categories

AI Development Platforms (5th)

Saturn Cloud

Ranking in Data Science Platforms

13th

Average Rating

10.0

Reviews Sentiment

7.5

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of July 2025, in the Data Science Platforms category, the mindshare of Amazon SageMaker is 6.2%, down from 9.3% 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 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."

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

"The superb thing that SageMaker brings is that it wraps everything well. It's got the deployment, the whole framework."

"The solution's ability to improve work at my organization stems from the ensemble model and a combination of various models it provides."

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

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

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

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."

"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

"When starting a new session, the waiting time can be quite long, ranging from two to five minutes."

"The entry point can be a bit difficult. Having all documentation easily accessible on the front page of SageMaker would be a great improvement."

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

"I would recommend having more walkthrough videos and articles beyond AWS Skill Builder."

"There is room for improvement in the collaboration with serverless architecture, particularly integration with AWS Lambda."

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

"Scalability to handle big data can be improved by making integration with networks such as Hadoop and Apache Spark easier."

"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."

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."

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

"We'd like to have the capability for installing more libraries."

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

"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."

Pricing and Cost Advice

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

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

"The solution is relatively cheaper."

"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."

"Amazon SageMaker is a very expensive product."

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

"The product is expensive."

More Amazon SageMaker pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

19%

Computer Software Company

11%

Manufacturing Company

University

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?

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

Amazon SageMaker

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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.

Amazon SageMaker vs. Saturn Cloud