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 7.7% mindshare in Data Science Platforms, compared to Domino Data Lab’s 2.6% mindshare. Additionally, 91% of Amazon Web Services (AWS) users are willing to recommend the solution, compared to 100% of Domino Data Lab users who would recommend it.

Amazon SageMaker

Read 36 Amazon SageMaker reviews

7,315 Views
5,868 Comparison Views

91% willing to recommend

Domino Data Science Platform

Read 2 Domino Data Science Platform reviews

1,921 Views
1,726 Comparison Views

100% willing to recommend

Amazon SageMaker

Domino Data Science Platform

Comparison Buyer's Guide

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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

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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)

Domino Data Science Platform

Ranking in Data Science Platforms

15th

Average Rating

7.6

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

No ranking in other categories

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 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

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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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…

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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 product aggregates everything we need to build and deploy machine learning models in one place."

"Allows you to create API endpoints."

"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."

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

"The most valuable feature of Amazon SageMaker for me is the model deployment service."

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

"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."

"I have seen a return on investment, probably a factor of four or five."

More Amazon SageMaker pros

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

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

Cons

"There are other better solutions for large data, such as Databricks."

"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."

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

"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 training modules could be enhanced. We had to take in-person training to fully understand SageMaker, and while the trainers were great, I think more comprehensive online modules would be helpful."

"The main challenge with Amazon SageMaker is the integrations."

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

"Having all documentation easily accessible on the front page of SageMaker would be a great improvement."

More Amazon SageMaker cons

"The predictive analysis feature needs improvement."

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

Pricing and Cost Advice

"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."

"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."

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

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

"The solution is relatively cheaper."

"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

18%

Educational Organization

14%

Computer Software Company

11%

Manufacturing Company

Financial Services Firm

33%

Manufacturing Company

11%

Insurance Company

Computer Software Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

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...

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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...

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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.

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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 cannot handle big deployments, which is not suitable for LLMs.

See all answers

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

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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 solely on data scientists might not be sufficient. I'd rate the solution eight out...

See all answers

Comparisons

Databricks vs Amazon SageMaker

Compared 26% 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 7% of the time

DataRobot 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 10% 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

Microsoft Azure Machine Learning Studio vs Domino Data Science Platform

Compared 5% of the time

More Domino Data Science Platform Competitors

Product Reports

Buyer's Guide

Amazon SageMaker

January 2025

Download Amazon SageMaker product report

Buyer's Guide

Data Science Platforms

December 2024

Download Domino Data Science Platform product report

Also Known As

AWS SageMaker, SageMaker

Domino Data Lab Platform

Learn More

Amazon Web Services (AWS)

Video not available

Domino Data Lab

Interactive Demo

Demo not available

Amazon Web Services (AWS)

Domino Data Lab

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.

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

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

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