Amazon SageMaker vs Starburst Galaxy comparison

Amazon Web Services (AWS) and Starburst are both solutions in the Data Science Platforms category. Amazon Web Services (AWS) is ranked #2 with an average rating of 8.0, while Starburst is ranked #9 with an average rating of 9.7. Amazon Web Services (AWS) holds a 5.7% mindshare in Data Science Platforms, compared to Starburst’s 0.8% 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 38 Amazon SageMaker reviews

6,422 Views
3,725 Comparison Views

91% willing to recommend

Starburst Galaxy

Read 9 Starburst Galaxy reviews

616 Views
542 Comparison Views

100% willing to recommend

Amazon SageMaker

Starburst Galaxy

Comparison Buyer's Guide

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Executive Summary

We performed a comparison between Amazon SageMaker and Starburst Galaxy based on real PeerSpot user reviews.

Find out in this report how the two Data Science Platforms solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

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

Buyer's Guide

Amazon SageMaker vs. Starburst Galaxy

September 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

2nd

Average Rating

7.8

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

AI Development Platforms (4th)

Starburst Galaxy

Ranking in Data Science Platforms

9th

Average Rating

9.8

Reviews Sentiment

1.0

Number of Reviews

Ranking in other categories

Streaming Analytics (12th)

Mindshare comparison

As of October 2025, in the Data Science Platforms category, the mindshare of Amazon SageMaker is 5.7%, down from 7.9% compared to the previous year. The mindshare of Starburst Galaxy is 0.8%, down from 0.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms Market Share Distribution
Product	Market Share (%)
Amazon SageMaker	5.7%
Starburst Galaxy	0.8%
Other	93.5%

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, such as Firehose for connecting to data pipelines, are simple to use. Tools like AWS Glue integrate well for data transformations. The Databricks integration aids data scientists and engineers. SageMaker is fully managed, offers high availability, flexibility with TensorFlow, PyTorch, and MXNet, and comes with pre-trained algorithms for forecasting, anomaly detection, and more.

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Stephen-Howard

Sr Director, Technology at Spreetail

Federated querying delivers integrated data at record speed and reduces processing time

The biggest win has been the ability to combine data from multiple sources and deliver it to the business at record speed. This capability has allowed us to query directly through Starburst Galaxy, enabling teams to access integrated data that would otherwise be hard to pull together. This has reduced both our ETL processing time and storage costs. We are answering questions that would have been hard, if not impossible, to answer previously because the data came from disparate, disconnected sources.

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

"Allows you to create API endpoints."

"The feature I found most valuable is the data catalog, as it assists with the lineage of data through the preparation pipeline."

"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."

"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."

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

"The evolution from SageMaker Classic to SageMaker Studio, particularly the UI part of Studio, is commendable."

"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."

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

More Amazon SageMaker pros

"Starburst on Trino, combined with our SQL-native data transformation tool SQLMesh, has delivered anywhere from a two to five times improvement in compute performance across our transformation DAG."

"Starburst Galaxy has improved our organization by unifying access to all major data sources, reducing the need for complex ETL processes."

"Starburst has provided us with virtually guaranteed performance on complex queries across datasets that are in the tens of gigabytes which complete in seconds."

"The most fundamental feature is the query engine, which is much faster than any of the competitors; Starburst is able to finish most queries within 10 seconds, which is especially important for many non-technical employees."

"Starburst has provided us with virtually guaranteed performance on complex queries across datasets that are in the tens of gigabytes which complete in seconds."

"Starburst Galaxy serves as our primary SQL-based data processing engine, a strategic decision driven by its seamless integration with our AWS cloud infrastructure and its ability to deliver high performance with low-latency responses."

"Starburst Galaxy is becoming a cornerstone of our data platform, empowering us to make smarter and faster decisions across the organization."

"Starburst Galaxy has improved our organization by unifying access to all major data sources, reducing the need for complex ETL processes."

Cons

"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."

"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."

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

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

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

"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."

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

More Amazon SageMaker cons

"Cluster startup time can be slow, sometimes taking over a minute."

"Multi-tenancy could be improved. In order to have multiple environments for SSO, we maintain multiple tenants that are connected to different AWS accounts via the Marketplace."

"Cluster startup time is another pain point, typically 3 to 5 minutes, which is not the worst with proper planning but can be annoying for ad-hoc work."

"I would like Starburst to leverage AI to improve usability. Data lakes are complicated and difficult for users to explore."

"The most persistent issue is the cluster spin-up time."

Pricing and Cost Advice

"There is no license required for the solution since you can use it on demand."

"The tool's pricing is reasonable."

"You don't pay for Sagemaker. You only pay for the compute instances in your storage."

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

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

"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."

"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

18%

Computer Software Company

11%

Manufacturing Company

University

Financial Services Firm

29%

Computer Software Company

14%

Government

Consumer Goods Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	12
Midsize Enterprise	11
Large Enterprise	16

By reviewers
Company Size	Count
Small Business	4
Midsize Enterprise	2
Large Enterprise	1

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?

If you manage it effectively, their pricing is reasonable. It's similar to anything in the cloud; if you don't manage it properly, it can be expensive, but if you do, it's fine.

See all answers

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

You pay for cluster uptime. It is important to be aggressive about autoscaling, as a single worker will get you a long way. I recommend never connecting a BI tool to your Galaxy cluster. Instead, w...

See all answers

What needs improvement with Starburst Galaxy?

As a hosted option, I wish I had more control over the cluster configuration, specifically regarding some of the more advanced options. Trino is extremely flexible and powerful, but some of this fu...

See all answers

What is your primary use case for Starburst Galaxy?

I use Starburst as a cost-efficient hosted option for Trino for data integration and ad-hoc analysis across a broad range of data sources. It is surprisingly useful to query SQL Server, a Google Sh...

See all answers

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Compared 52% of the time

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Compared 29% of the time

Dremio vs Starburst Galaxy

Compared 19% of the time

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Product Reports

Buyer's Guide

Amazon SageMaker

October 2025

Download Amazon SageMaker product report

Buyer's Guide

Starburst Galaxy

September 2025

Download Starburst Galaxy product report

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 Galaxy offers advanced analytics solutions tailored for big data environments. It is renowned for its scalability and flexibility, enabling data-driven decisions.

Starburst Galaxy provides an efficient platform for conducting complex data analysis across multiple datasets. Its architecture facilitates seamless integration with existing data infrastructure, allowing organizations to harness the value of their data efficiently. Users appreciate its ability to perform intricate queries without the need for extensive data movement, fostering a more agile approach to data management and analysis.

What features make Starburst Galaxy unique?

Advanced Query Engine: Optimizes data processing to handle complex analytics efficiently.
Scalability: Easily adapts to growing data needs without compromising performance.
Integration Capabilities: Seamlessly connects with existing data systems for streamlined operations.

What benefits and ROI should users look for?

Cost Efficiency: Reduces storage and compute costs by optimizing queries and minimizing data movement.
Improved Decision-Making: Empowers users with quick access to critical insights for strategic planning.
Enhanced Performance: Delivers faster query responses, boosting productivity and time-to-value.

Starburst Galaxy is instrumental in industries such as finance, healthcare, and e-commerce, where large-scale data analysis is crucial. Organizations in these fields implement its features to accelerate data processing and gain a competitive advantage, turning vast amounts of information into actionable insights that drive industry-specific innovations.

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 Galaxy

September 2025

Free Report: Amazon SageMaker vs. Starburst Galaxy

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