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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 8.0, 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 2.5% 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
  • 6,544 Views
  • 5,235 Comparison Views
91% willing to recommend
Domino Data Science Platform
Read 2 Domino Data Science Platform reviews
  • 1,745 Views
  • 1,555 Comparison Views
100% willing to recommend
 

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.

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To learn more, read our detailed Amazon SageMaker vs. Domino Data Science Platform Report (Updated: March 2025).
Buyer's Guide
Amazon SageMaker vs. Domino Data Science Platform
March 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
3rd
Average Rating
7.8
Reviews Sentiment
7.1
Number of Reviews
36
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
2
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of March 2025, in the Data Science Platforms category, the mindshare of Amazon SageMaker is 7.5%, down from 9.9% compared to the previous year. The mindshare of Domino Data Science Platform is 2.5%, down from 2.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Science Platforms
 

Featured Reviews

Hemant Paralkar - PeerSpot reviewer
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.
Read full review
AS
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

"The product aggregates everything we need to build and deploy machine learning models in one place."
"The Autopilot feature is really good because it's helpful for people who don't have much experience with coding or data pipelines. When we suggest SageMaker to clients, they don't have to go through all the steps manually. They can leverage Autopilot to choose variables, run experiments, and monitor costs. The results are also pretty accurate."
"The most valuable features include the ML operations that allow for designing, deploying, testing, and evaluating models."
"I have contacted the solution's technical support, and they were really good. I rate the technical support a ten out of ten."
"The technical support of the tool was good."
"The solution's ability to improve work at my organization stems from the ensemble model and a combination of various models it provides."
"The solution is easy to scale...The documentation and online community support have been sufficient for us so far."
"Allows you to create API endpoints."
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

"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."
"SageMaker would be improved with the addition of reporting services."
"Having all documentation easily accessible on the front page of SageMaker would be a great improvement."
"I would say the IDE is quite immature, but it is still in its infancy, so I expect it to get better over time."
"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."
"AI is a new area and AWS needs to have an internship training program available."
"Amazon might need to emphasize its capabilities in generative models more effectively."
More Amazon SageMaker cons
"The deployment of large language models (LLMs) could be improved."
"The predictive analysis feature needs improvement."
 

Pricing and Cost Advice

"The support costs are 10% of the Amazon fees and it comes by default."
"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."
"The pricing could be better, especially for querying. The per-query model feels expensive."
"SageMaker is worth the money for our use case."
"There is no license required for the solution since you can use it on demand."
"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."
"The cost offers a pay-as-you-go pricing model. It depends on the instance that you do."
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
13%
Computer Software Company
11%
Manufacturing Company
8%
Financial Services Firm
34%
Manufacturing Company
11%
Insurance Company
10%
Computer Software Company
7%
 

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...
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 ChatGPT that requires models. We utilize Amazon SageMaker to create endpoints for t...
See all answers
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.
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 cannot handle big deployments, which is not suitable for LLMs.
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-end development processes. Domino is based on Git, enabling collaboration similar...
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 solely on data scientists might not be sufficient. I'd rate the solution eight out...
See all answers
 

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Product Reports

Buyer's Guide
Amazon SageMaker
March 2025
Amazon SageMaker reportDownload Amazon SageMaker product report
Buyer's Guide
Data Science Platforms
February 2025
Domino Data Science Platform reportDownload Domino Data Science Platform product report
 

Also Known As

AWS SageMaker, SageMaker
Domino Data Lab Platform
 

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.

Amazon Web Services (AWS)

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.

Domino Data Lab
 

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
March 2025
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: March 2025.
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