Amazon SageMaker vs Saturn Cloud comparison

Amazon Web Services (AWS) and Saturn Cloud are both solutions in the Data Science Platforms category. Amazon Web Services (AWS) is ranked #3 with an average rating of 7.9, while Saturn Cloud is ranked #10 with an average rating of 10.0. Amazon Web Services (AWS) holds a 7.5% mindshare in Data Science Platforms, compared to Saturn Cloud’s 0.2% mindshare. Additionally, 90% of Amazon Web Services (AWS) users are willing to recommend the solution, compared to 100% of Saturn Cloud users who would recommend it.

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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).

Buyer's Guide

Amazon SageMaker vs. Saturn Cloud

December 2024

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Categories and Ranking

IBM SPSS Statistics

Mindshare comparison

As of December 2024, in the Data Science Platforms category, the mindshare of IBM SPSS Statistics is 2.7%, up from 2.7% compared to the previous year. The mindshare of Amazon SageMaker is 7.5%, down from 10.1% compared to the previous year. The mindshare of Saturn Cloud is 0.2%, up from 0.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms

Featured Reviews

Md Masudul Hassan

Agricultural Finance and IT Researcher at Sher-e-Bangla Agricultural University

Comprehensive data analysis capabilities with a user-friendly interface, providing an efficient and reliable platform for researchers and analysts

I believe that offering short-term SPSS licenses, perhaps when customer sourcing is available, could make it more affordable. These licenses shouldn't include features tailored for universities or large sales organizations. Instead, they could offer discounts or additional facilities for smaller entities to access the software. In developing countries, it would be beneficial to provide certain features to users at no cost initially, while also customizing pricing options. For example, offering basic features to the first hundred users can help them become familiar with the software and its capabilities. This approach encourages users to upgrade to higher tiers as they become more experienced and require additional functionality.

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Natu Lauchande

Tech Lead - Sanlam Fintech Cluster - Data,ML,AI Eng. at Sanlam

Easy to use and manage, but the documentation does not have a lot of information

SageMaker Studio sounds very interesting. Feature Store and data pipeline features are very interesting. The product is a one-stop shop. It allows people without much engineering knowledge to try out and deploy models in environments similar to the production environments. The tool makes our ML model development a bit more efficient because everything is in one environment. It is easy to manage compared to when things were in different components of AWS. Amazon SageMaker is in AWS, so I need not pay two bills. It is one less system to manage, so it is easier.

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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

"Custom tables and macros: They allow us to create useful reports quickly for a broad audience."

"It offers very good visualization."

"You can quickly build models because it does the work for you."

"Capability analysis is one of the main and valuable functions. We also do some hypothesis testing in Minitab and summary stats. These are the functions that we find very useful."

"The solution is very comprehensive, especially compared to Minitabs, which is considered more for manufacturing. However, whatever data you want to analyze can be handled with SPSS."

"The most valuable feature is its robust statistical analysis capabilities."

"The features that I have found most valuable are the Bayesian statistics and descriptive statistics."

"The most valuable feature is the user interface because you don't need to write code."

More IBM SPSS Statistics pros

"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."

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

"It's user-friendly for business teams as they can understand many aspects through the AWS interface."

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

"The feature I found most valuable is the data catalog, as it assists with the lineage of data through the preparation pipeline."

"We were able to use the product to automate processes."

"The technical support of the tool was good."

"I have contacted the solution's technical support, and they were really good. I rate the technical support a ten out of ten."

More Amazon SageMaker pros

"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."

"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."

"It didn't take long to see that Saturn Cloud could scale with my needs, providing more resources when required."

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

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

Cons

"It could allow adding color to data models to make them easier to interpret."

"In developing countries, it would be beneficial to provide certain features to users at no cost initially, while also customizing pricing options."

"Technical support needs some improvement, as they do not respond as quickly as we would like."

"The product should provide more ways to import data and export results that are user-friendly for high-level executives."

"Needs more statistical modelling functions."

"Each algorithm could be more adaptable to some industry-specific areas, or, in some cases, adapted for maintenance."

"The solution needs to improve forecasting using time series analysis."

"Improvements are needed in the user interface, particularly in terms of user-friendliness."

More IBM SPSS Statistics cons

"The platform could be more accessible to users with basic coding skills, making it more intuitive and easier for beginners to use comfortably."

"While integration is available, there are concerns about how secure this integration is, particularly when exposing data to SageMaker."

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

"The solution requires a lot of data to train the model."

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

"Amazon SageMaker can make it simpler to manage the data flow from start to finish, such as by integrating data, usingthe machine, and deploying models. This process could be more user-friendly compared to other tools. I would also like to improve integration with Bedrock and the LLM connection for AWS."

"The dashboard could be improved by including more features and providing more information about deployed models, their drift, performance, scaling, and customization options."

"I had to create custom templates for labeling multi-data sets, such as text and images, which was time-consuming."

More Amazon SageMaker cons

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

"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."

"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

"I rate the tool's pricing a five out of ten."

"The pricing of the modeler is high and can reduce the utility of the product for those who can not afford to adopt it."

"Our licence is on a yearly renewal basis. While pricing is not the primary concern in our evaluation, as products are assessed by whether they can meet our user needs and expertise, the cost can be a limiting factor in the number of licences we procure."

"The price of IBM SPSS Statistics could improve."

"While the pricing of the product may be higher, the accompanying service and features justify the investment."

"If it requires lot of data processing, maybe switching to IBM SPSS Clementine would be better for the buyer."

"SPSS is an expensive piece of software because it's incredibly complex and has been refined over decades, but I would say it's fairly priced."

"We think that IBM SPSS is expensive for this function."

More IBM SPSS Statistics pricing and cost advice

"Databricks solution is less costly than Amazon SageMaker."

"Amazon SageMaker is a very expensive product."

"The pricing is comparable."

"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 solution is relatively cheaper."

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

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

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

More Amazon SageMaker pricing and cost advice

Information not available

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Top Industries

By visitors reading reviews

Financial Services Firm

17%

Computer Software Company

University

Manufacturing Company

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

What do you like most about IBM SPSS Statistics?

The software offers consistency across multiple research projects helping us with predictive analytics capabilities.

See all answers

What is your experience regarding pricing and costs for IBM SPSS Statistics?

The cost of IBM SPSS Statistics is managed by organizations, not individual researchers. It is a very expensive produ...

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What needs improvement with IBM SPSS Statistics?

IBM SPSS Statistics does not keep you close to your data like KNIME. In KNIME, at every stage, you can see the result...

See all answers

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 designe...

See all answers

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 Cha...

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What is your experience regarding pricing and costs for Amazon SageMaker?

Pricing is rated as a six, which is slightly more expensive compared to the budget yet adequate for the capabilities ...

See all answers

What do you like most about Saturn Cloud?

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

See all answers

What needs improvement with Saturn Cloud?

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

See all answers

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...

See all answers

Comparisons

IBM SPSS Modeler vs IBM SPSS Statistics

Compared 16% of the time

Alteryx vs IBM SPSS Statistics

Compared 13% of the time

TIBCO Statistica vs IBM SPSS Statistics

Compared 12% of the time

Microsoft Azure Machine Learning Studio vs IBM SPSS Statistics

Compared 12% of the time

Google Cloud Datalab vs IBM SPSS Statistics

Compared 6% of the time

More IBM SPSS Statistics Competitors

Databricks vs Amazon SageMaker

Compared 27% of the time

Azure OpenAI vs Amazon SageMaker

Compared 12% of the time

Google Vertex AI vs Amazon SageMaker

Compared 10% of the time

Domino Data Science Platform vs Amazon SageMaker

Compared 7% of the time

Dataiku vs Amazon SageMaker

Compared 6% of the time

More Amazon SageMaker Competitors

No data available

Product Reports

Buyer's Guide

IBM SPSS Statistics

December 2024

Download IBM SPSS Statistics product report

Buyer's Guide

Amazon SageMaker

December 2024

Download Amazon SageMaker product report

Buyer's Guide

Saturn Cloud

November 2024

Download Saturn Cloud product report

Also Known As

SPSS Statistics

AWS SageMaker, SageMaker

No data available

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IBM

Amazon Web Services (AWS)

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Saturn Cloud

Overview

IBM SPSS Statistics is a powerful data mining solution that is designed to aid business leaders in making important business decisions. It is designed so that it can be effectively utilized by organizations across a wide range of fields. SPSS Statistics allows users to leverage machine learning algorithms so that they can mine and analyze data in the most effective way possible.

IBM SPSS Statistics Benefits

Some of the ways that organizations can benefit by choosing to deploy IBM SPSS Statistics include:

Ease of use. SPSS Statistics enables users to simply and intuitively take control of their statistical needs. The solution is designed so that analysts who do not know how to code can easily make full use of the various tools and capabilities that SPSS Statistics has to offer. Its command language is so straightforward that it does not require users to undergo special training before they use it.

Comprehensive and flexible build. SPSS Statistics is designed to be both a comprehensive and highly flexible analytics solution. It enables users to utilize a variety of integrations that make it easy for users to add features that they might feel they are missing.

Automation. SPSS Statistics makes it simple for users to automate basic tasks that they might otherwise devote too much time worrying about. Tasks like calculation or data gathering can be delegated to the system while more conceptual tasks like data analysis are given to an organization’s analysts to handle.

IBM SPSS Statistics Features

Intuitive user interface. SPSS Statistics enables users to deploy an intuitive interface that makes the process of system management simple. Among the other components of this interface is a drag-and-drop feature that makes analysis and management possible for anyone who wants to use it.

Advanced data visualizations. Analysts that employ SPSS Statistics gain access to tools that empower them to create and export data visualizations. These visualizations can be formatted in many different ways depending on what the user needs.

Local data storage. SPSS Statistics has the ability to securely store data on a user’s computer. This enables them to add layers of security that would not necessarily be present if the data was stored in the cloud.

Reviews from Real Users

IBM SPSS Statistics is a highly effective solution that stands out when compared to many of its competitors. Two major advantages it offers are the wealth of functionalities that it provides and its high level of accessibility.

An Emeritus Professor of Health Services Research at a university writes, "The most valuable feature of IBM SPSS Statistics is all the functionality it provides. Additionally, it is simple to do the five-way analysis that you can in a multidimensional setup space. It's the multidimensional space facility that is most useful."

A Director of Systems Management & MIS Operations at a university, says, “The SPSS interface is very accessible and user-friendly. It's really easy to get information from it. I've shared it with experts and beginners, and everyone can navigate it.”

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

LDB Group, RightShip, Tennessee Highway Patrol, Capgemini Consulting, TEAC Corporation, Ironside, nViso SA, Razorsight, Si.mobil, University Hospitals of Leicester, CROOZ Inc., GFS Fundraising Solutions, Nedbank Ltd., IDS-TILDA

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.

Buyer's Guide

Amazon SageMaker vs. Saturn Cloud

December 2024

Free Report: Amazon SageMaker vs. Saturn Cloud

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