Amazon SageMaker vs Starburst Enterprise comparison

Amazon Web Services (AWS) and Starburst are both solutions in the Data Science Platforms category. Amazon Web Services (AWS) is ranked #3 with an average rating of 8.0, while Starburst is ranked #12 with an average rating of 8.5. Amazon Web Services (AWS) holds a 6.2% mindshare in Data Science Platforms, compared to Starburst’s 2.2% mindshare. Additionally, 91% of Amazon Web Services (AWS) users are willing to recommend the solution, compared to 100% of Starburst users who would recommend it.

Amazon SageMaker

Read 37 Amazon SageMaker reviews

4,738 Views
1,603 Comparison Views

91% willing to recommend

Starburst Enterprise

Read 2 Starburst Enterprise reviews

1,143 Views
535 Comparison Views

100% willing to recommend

Amazon SageMaker

Starburst Enterprise

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Executive SummaryUpdated on Dec 5, 2024

Amazon SageMaker and Starburst Enterprise are competing products in advanced data analytics and machine learning. Amazon SageMaker seems to have an upper hand in pricing and support, while Starburst Enterprise offers a strong feature set, making it appealing despite higher costs.

Features: Amazon SageMaker provides comprehensive machine learning tools such as model training, deployment, and management, along with scalable infrastructure. It integrates tightly with AWS services. Starburst Enterprise is strong with its SQL engine and data federation capabilities, enabling cross-platform analytics and handling queries from different data sources efficiently.

Ease of Deployment and Customer Service: Amazon SageMaker facilitates seamless deployment within the AWS ecosystem, simplifying setup and scaling. It offers extensive AWS support and resources. Starburst Enterprise can run on various platforms including on-premises and cloud, but may involve more complex setup. It provides tailored support and consultancy services, emphasizing direct customer interaction, which benefits complex deployments.

Pricing and ROI: Amazon SageMaker offers a pay-as-you-go pricing model, making it cost-effective for variable usage and providing significant AWS credits for startups, resulting in a favorable ROI. Starburst Enterprise requires a higher initial setup cost, however, it offers a compelling ROI by efficiently querying data across multiple sources, which reduces the need for costly data migration and duplication.

To learn more, read our detailed Amazon SageMaker vs. Starburst Enterprise Report (Updated: June 2025).

Buyer's Guide

Amazon SageMaker vs. Starburst Enterprise

June 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

Ranking in other categories

AI Development Platforms (5th)

Starburst Enterprise

Ranking in Data Science Platforms

12th

Average Rating

8.6

Reviews Sentiment

6.9

Number of Reviews

Ranking in other categories

Streaming Analytics (14th)

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 Starburst Enterprise is 2.2%, up from 1.7% 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.

Read full review

KamleshPant

Senior Software Architect at USEReady

Connects to any data source from any region and offers unified access

There are no specific projects supported by Starburst regarding AI initiatives or machine learning projects. In the future, if we have all the data available, we can definitely capitalize on AI/ML and LLM capabilities to summarize data and gain insights. That's our future goal, but we haven't reached that point yet. There should be support for REST API data sources to access data from the web. We often have data coming in and communicate with data sources via REST API calls. I don't see that capability in Starburst currently; everything is through JDBC or ODBC. If Starburst could seamlessly access data using REST API capabilities, it would be a game-changer. The self-service data management features, like self-service materialized views, are great, but they can be a bit complex for basic users to understand.

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

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

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

"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 contacted the solution's technical support, and they were really good. I rate the technical support a ten out of ten."

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

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

"Allows you to create API endpoints."

"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 these models, making accessing them convenient as needed."

More Amazon SageMaker pros

"It's very scalable, fast performing, and supports many catalogs."

"We have noticed improvements in performance using Starburst Enterprise. It handles complex data, including reading and partitioning files. We can add a new catalog to Starburst Enterprise by providing connection details and service account information. This allows us to integrate with existing tools, such as the Snowflake database, which we use for data protection in our project."

Cons

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

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

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

"The product must provide better documentation."

"In my opinion, one improvement for Amazon SageMaker would be to offer serverless GPUs. Currently, we incur costs on an hourly basis. It would be beneficial if the tool could provide pay-as-you-go pricing based on endpoints."

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

"The payment and monitoring metrics are a bit confusing not only for Amazon SageMaker but also for the range of other products that fall under AWS, especially for a new user of the product."

"Improvement is needed in the no-code and low-code capabilities of Amazon SageMaker."

More Amazon SageMaker cons

"There should be support for REST API data sources to access data from the web."

"Starburst Enterprise could improve by offering additional features similar to those provided by other SQL query tools. For example, incorporating functionalities like pivot tables would make it more feasible to use."

Pricing and Cost Advice

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

"Amazon SageMaker is a very expensive product."

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

"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."

"The support costs are 10% of the Amazon fees and it comes by default."

"The solution is relatively cheaper."

"The pricing is comparable."

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

More Amazon SageMaker pricing and cost advice

"I haven't personally dealt with the pricing aspects first-hand, but from what I understand, it largely depends on the specifics of your setup, especially the machines you use on AWS. The cost of using Starburst Enterprise can vary based on the amount of data you're processing and the type of machines you opt for, whether on AWS or another cloud platform."

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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

44%

Computer Software Company

Government

Energy/Utilities 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...

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?

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...

See all answers

What is your experience regarding pricing and costs for Starburst Enterprise?

I haven't personally dealt with the pricing aspects first-hand, but from what I understand, it largely depends on the specifics of your setup, especially the machines you use on AWS. The cost of us...

See all answers

What needs improvement with Starburst Enterprise?

See all answers

What is your primary use case for Starburst Enterprise?

We use Starburst with one client who is exploring their ecosystem to remove data silos and enable data access across departments. It's a very big ecosystem, like a finance institute. They are curre...

See all answers

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Compared 45% of the time

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Compared 14% of the time

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Compared 13% of the time

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Compared 7% of the time

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Product Reports

Buyer's Guide

Amazon SageMaker

June 2025

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Buyer's Guide

Data Science Platforms

June 2025

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Also Known As

AWS SageMaker, SageMaker

No data available

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)

Starburst Enterprise is a data analytics platform that enables organizations to access and analyze data from multiple sources, including cloud-based and on-premises data warehouses. It provides a single access point to all data sources, allowing users to query and analyze data without moving it between systems.

By providing a unified view, Starburst Enterprise helps organizations make better-informed decisions and improve operational efficiency, leading to better customer insights and more accurate forecasting. Overall, Starburst Enterprise is a powerful tool for organizations looking to unlock the full potential of their data.

Starburst

Sample Customers

DigitalGlobe, Thomson Reuters Center for AI and Cognitive Computing, Hotels.com, GE Healthcare, Tinder, Intuit

Information Not Available

Buyer's Guide

Amazon SageMaker vs. Starburst Enterprise

June 2025

Free Report: Amazon SageMaker vs. Starburst Enterprise

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