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

14th

Average Rating

7.6

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of July 2025, in the Data Science Platforms category, the mindshare of Amazon SageMaker is 6.2%, down from 9.3% compared to the previous year. The mindshare of Domino Data Science Platform is 2.7%, down from 2.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms

Featured Reviews

Saurabh Jaiswal

Python AWS & AI Expert at a tech consulting company

Create innovative assistants with seamless data integration for large-scale projects

The various integration options available in Amazon SageMaker ( /products/amazon-sagemaker-reviews ), such as Firehose for connecting to data pipelines, are simple to use. Tools like AWS Glue ( /products/aws-glue-reviews ) integrate well for data transformations. The Databricks ( /products/databricks-reviews ) integration aids data scientists and engineers. SageMaker is fully managed, offers high availability, flexibility with TensorFlow ( /products/tensorflow-reviews ), PyTorch ( /products/pytorch-reviews ), and MXNet ( /products/mxnet-reviews ), and comes with pre-trained algorithms for forecasting, anomaly detection, and more.

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

"The support is very good with well-trained engineers whose training curriculum is rigorous."

"SageMaker supports building, training, and deploying AI models from scratch, which is crucial for my ML project."

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

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

"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."

"They offer insights into everyone making calls in my organization."

"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

"AI is a new area and AWS needs to have an internship training program available."

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

"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."

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

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

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

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

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

More Amazon SageMaker cons

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

"The predictive analysis feature needs improvement."

Pricing and Cost Advice

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

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

"The solution is relatively cheaper."

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

"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."

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

"Amazon SageMaker is a very expensive product."

More Amazon SageMaker pricing and cost advice

Information not available

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Top Industries

By visitors reading reviews

Financial Services Firm

19%

Computer Software Company

12%

Manufacturing Company

Educational Organization

Financial Services Firm

38%

Manufacturing Company

10%

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...

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?

The pricing is high, around an eight. However, SageMaker offers free trials for the first two months, allowing users to determine which features they need. It is considered value for money given it...

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