Amazon SageMaker vs Domino Data Science Platform comparison

Amazon Web Services (AWS) and Domino Data Lab are both solutions in the Data Science Platforms category. Amazon Web Services (AWS) is ranked #3 with an average rating of 7.9, while Domino Data Lab is ranked #15 with an average rating of 8.0. Amazon Web Services (AWS) holds a 7.5% mindshare in Data Science Platforms, compared to Domino Data Lab’s 2.6% mindshare. Additionally, 90% of Amazon Web Services (AWS) users are willing to recommend the solution, compared to 100% of Domino Data Lab users who would recommend it.

Comparison Buyer's Guide

Download the report

Executive SummaryUpdated on Dec 5, 2024

Domino Data Science Platform and Amazon SageMaker are competing solutions in data science and machine learning platforms. Amazon SageMaker is viewed as the more feature-rich product because of its comprehensive capabilities and integration with AWS.

Features: Domino focuses on user-centric features including collaboration capabilities, version control, and team-focused tools. Amazon SageMaker offers automated machine learning processes, extensive integration with other AWS services, and enhanced machine learning lifecycle management.

Ease of Deployment and Customer Service: Amazon SageMaker benefits from a scalable and integrative deployment model, leveraging AWS's global infrastructure and an extensive support ecosystem. Domino emphasizes easy-to-use deployment mechanisms and collaborative workspaces, but its support system is less extensive.

Pricing and ROI: Domino typically requires a higher initial setup cost but offers ROI for organizations focusing on team collaboration. Amazon SageMaker provides flexible pricing aligned with cloud usage, favoring organizations deeply integrated with AWS and offering a scalable pay-as-you-go model.

To learn more, read our detailed Amazon SageMaker vs. Domino Data Science Platform Report (Updated: November 2024).

Buyer's Guide

Amazon SageMaker vs. Domino Data Science Platform

November 2024

Download the complete report

Helped 823,875 peers since 2012

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 Domino Data Science Platform is 2.6%, down from 2.7% 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.

Read full review

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.

Read full review

AHMEDSAHRAOUI

Machine Learning Engineer at Unemployed

Accelerated machine learning model development with seamless deployment

We used Domino Data Science Platform for developing and working with machine learning models. It facilitated end-to-end development processes. Domino is based on Git, enabling collaboration similar to using Git. Each user operates on their own equivalent of a branch or fork, and once finished, they…

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

"In terms of the features I've found most valuable, I'd say the duration, the correlation, and of course the nonparametric statistics. I use it for reliability and survival analysis, time series, regression models in different solutions, and different types of solutions."

"SPSS is quite robust and quicker in terms of providing you the output."

"The solution has numerous valuable features. We particularly like custom tabs. It's very useful. We end up analyzing a lot of software data, so features related to custom tabs are really helpful."

"It has the ability to easily change any variable in our research."

"They have many existing algorithms that we can use and use effectively to analyze and understand how to put our data to work to improve what we do."

"IBM SPSS Statistics depends on AI."

"Most of the product features are good but I particularly like the linear regression analysis."

"The best part is that they have an algorithm handbook, so you can open it up and understand how it works, and if it is useful, this is very important."

More IBM SPSS Statistics pros

"The most valuable features are the ability to store artifacts and gather reports and measures from experiments."

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

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

"The most valuable feature of Amazon SageMaker is its integration. For example, AWS Lambda. Additionally, we can write Python code."

"The most valuable features in Amazon SageMaker are its AutoML, feature store, and automated hyperparameter tuning capabilities."

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

"The most valuable feature of Amazon SageMaker is SageMaker Studio."

"The most tool's valuable feature, in my experience, is hyperparameter tuning. It allows us to test different parameters for the same model in parallel, which helps us quickly identify the configuration that yields the highest accuracy. This parallel computing capability saves us a lot of time."

More Amazon SageMaker pros

"The workspaces, which are like wrappers of Docker containers, made it easy to start development environments using Domino."

"The scalability of the solution is good; I'd rate it four out of five."

Cons

"IBM SPSS Statistics could improve the visual outputs where you are producing, for example, a graph for a company board of directors, or an advert."

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

"IBM SPSS Statistics does not keep you close to your data like KNIME."

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

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

"There is a learning curve; it's not very steep, but there is one."

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

"If there is any self-generation data collection plan (DCP), it would be helpful in gathering data. It would also be useful if there is a function to scale it up to, let's say, UiPath and have it consolidate and integrate into a UiPath solution."

More IBM SPSS Statistics cons

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

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

"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."

"For any cloud provider, the cost has to be substantially reduced, especially in the case of Amazon SageMaker, which is extremely expensive for huge workloads."

"The user interface (UI) and user experience (UX) of SageMaker and AWS, in general, need improvement as they are not intuitive and require substantial time to learn how to use specific services."

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

"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."

More Amazon SageMaker cons

"The predictive analysis feature needs improvement."

"The deployment of large language models (LLMs) could be improved."

Pricing and Cost Advice

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

"More affordable training for new staff members."

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

"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."

"It's quite expensive, but they do a special deal for universities."

"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."

More IBM SPSS Statistics 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."

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

"Databricks solution is less costly than Amazon SageMaker."

"On a scale from one to ten, where one is cheap, and ten is expensive, I rate the solution's pricing a six out of ten."

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

"The solution is relatively cheaper."

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

823,875 professionals have used our research since 2012.

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

Financial Services Firm

32%

Manufacturing Company

11%

Computer Software Company

Insurance Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

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...

See all answers

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...

See all answers

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 needs improvement with Domino Data Science Platform?

The deployment of large language models (LLMs) could be improved. Currently, Domino provides a simple server that can...

See all answers

What is your primary use case for Domino Data Science Platform?

We used Domino Data Science Platform for developing and working with machine learning models. It facilitated end-to-e...

See all answers

What advice do you have for others considering Domino Data Science Platform?

It's important to have a DevOps team well-versed with cloud-native solutions to manage Domino effectively. Relying so...

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

Dataiku vs Amazon SageMaker

Compared 6% of the time

Hugging Face vs Amazon SageMaker

Compared 5% of the time

More Amazon SageMaker Competitors

Databricks vs Domino Data Science Platform

Compared 28% of the time

Dataiku vs Domino Data Science Platform

Compared 9% of the time

Microsoft Azure Machine Learning Studio vs Domino Data Science Platform

Compared 7% of the time

Alteryx vs Domino Data Science Platform

Compared 6% of the time

KNIME vs Domino Data Science Platform

Compared 5% of the time

More Domino Data Science Platform Competitors

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

Data Science Platforms

December 2024

Download Domino Data Science Platform product report

Also Known As

SPSS Statistics

AWS SageMaker, SageMaker

Domino Data Lab Platform

Learn More

Video not available

IBM

Amazon Web Services (AWS)

Video not available

Domino Data Lab

Interactive Demo

Demo not available

IBM

Demo not available

Amazon Web Services (AWS)

Domino Data Lab

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.

Domino provides a central system of record that keeps track of all data science activity across an organization. Domino helps data scientists seamlessly orchestrate AWS hardware and software toolkits, increase flexibility and innovation, and maintain required IT controls and standards. Organizations can automatically keep track of all data, tools, experiments, results, discussion, and models, as well as dramatically scale data science investments and impact decision-making across divisions. The platform helps organizations work faster, deploy results sooner, scale rapidly, and reduce regulatory and operational risk.

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

Allstate, GSK, AstraZeneca, Federal Reserve, US Navy, Bristol Myers Squibb, Bayer, BNP Paribas, Moodys, New York Life

Buyer's Guide

Amazon SageMaker vs. Domino Data Science Platform

November 2024

Free Report: Amazon SageMaker vs. Domino Data Science Platform

Find out what your peers are saying about Amazon SageMaker vs. Domino Data Science Platform and other solutions. Updated: November 2024.

DOWNLOAD NOW

823,875 professionals have used our research since 2012.

See our Amazon SageMaker vs. Domino Data Science Platform report.

See our list of best Data Science Platforms vendors.

We monitor all Data Science Platforms reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.