Amazon SageMaker vs Domino Data Science Platform comparison

Amazon SageMaker vs. Domino Data Science Platform

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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 (5th)

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 April 2025, in the Data Science Platforms category, the mindshare of Amazon SageMaker is 7.3%, down from 9.8% compared to the previous year. The mindshare of Domino Data Science Platform is 2.5%, 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 solution is easy to scale...The documentation and online community support have been sufficient for us so far."

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

"I recommend SageMaker for ML projects if you need to build models from scratch."

"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."

"The most valuable feature of Amazon SageMaker is that you don't have to do any programming in order to perform some of your use cases."

"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."

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

"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

"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

"Amazon might need to emphasize its capabilities in generative models more effectively."

"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 for improvement is the pricing, which can be quite high."

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

"The solution needs to be cheaper since it now charges per document for extraction."

"Amazon SageMaker could improve in the area of hyperparameter tuning by offering more automated suggestions and tips during the tuning process."

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

"Improvements are needed in terms of complexity, data security, and access policy integration in Amazon SageMaker."

More Amazon SageMaker cons

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

"The predictive analysis feature needs improvement."

Pricing and Cost Advice

"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 tool's pricing is reasonable."

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

"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."

"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."

More Amazon SageMaker pricing and cost advice

Information not available

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Top Industries

By visitors reading reviews

Financial Services Firm

19%

Educational Organization

13%

Computer Software Company

11%

Manufacturing Company

Financial Services Firm

35%

Manufacturing Company

11%

Insurance Company

10%

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...

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?

Before deploying SageMaker, I reviewed the pricing, especially for notebook instances. The cost for small to medium instances is not very high.

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.

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

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...

Databricks vs Amazon SageMaker

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Product Reports

Amazon SageMaker

Download Amazon SageMaker product report

Data Science Platforms

Download 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

Amazon SageMaker vs. Domino Data Science Platform