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 7.9, while Starburst is ranked #14 with an average rating of 8.5. Amazon Web Services (AWS) holds a 7.6% mindshare in Data Science Platforms, compared to Starburst’s 2.1% 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 36 Amazon SageMaker reviews

7,315 Views
5,868 Comparison Views

91% willing to recommend

Starburst Enterprise

Read 2 Starburst Enterprise reviews

1,282 Views
1,140 Comparison Views

100% willing to recommend

Amazon SageMaker

Starburst Enterprise

Comparison Buyer's Guide

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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: January 2025).

Buyer's Guide

Amazon SageMaker vs. Starburst Enterprise

January 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 (4th)

Starburst Enterprise

Ranking in Data Science Platforms

14th

Average Rating

8.6

Reviews Sentiment

6.9

Number of Reviews

Ranking in other categories

Streaming Analytics (12th)

Mindshare comparison

As of February 2025, in the Data Science Platforms category, the mindshare of Amazon SageMaker is 7.6%, down from 9.9% compared to the previous year. The mindshare of Starburst Enterprise is 2.1%, up from 1.6% 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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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.

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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 is its integration. For example, AWS Lambda. Additionally, we can write Python code."

"One of the most valuable features of Amazon SageMaker for me is the one-touch deployment, which simplifies the process greatly."

"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."

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

"Feature Store, CodeCommit, versioning, model control, and CI/CD pipelines are the most valuable features in Amazon SageMaker."

"SageMaker offers functionalities like Jupyter Notebooks for development, built-in algorithms, model tuning, and options to deploy models on managed infrastructure."

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

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

More Amazon SageMaker pros

"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."

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

Cons

"The training modules could be enhanced. We had to take in-person training to fully understand SageMaker, and while the trainers were great, I think more comprehensive online modules would be helpful."

"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."

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

"Creating notebook instances for multiple users is pretty expensive in Amazon SageMaker."

"I would suggest that Amazon SageMaker provide free slots to allow customers to practice, such as a free slot to try out working with a Sandbox."

"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."

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

"The main challenge with Amazon SageMaker is the integrations."

More Amazon SageMaker cons

"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."

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

Pricing and Cost Advice

"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 solution is relatively cheaper."

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

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

"The product is expensive."

"Databricks solution is less costly than Amazon SageMaker."

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

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

18%

Educational Organization

15%

Computer Software Company

11%

Manufacturing Company

Financial Services Firm

44%

Computer Software Company

10%

Energy/Utilities Company

Government

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...

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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...

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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 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

Comparisons

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Compared 25% of the time

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Compared 12% of the time

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Compared 9% of the time

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Compared 8% of the time

Domino Data Science Platform vs Amazon SageMaker

Compared 7% of the time

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Compared 34% of the time

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Compared 21% of the time

Starburst Galaxy vs Starburst Enterprise

Compared 18% of the time

Alteryx vs Starburst Enterprise

Compared 12% of the time

Apache Spark Streaming vs Starburst Enterprise

Compared 5% of the time

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Product Reports

Buyer's Guide

Amazon SageMaker

February 2025

Download Amazon SageMaker product report

Buyer's Guide

Data Science Platforms

January 2025

Download Starburst Enterprise 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 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

January 2025

Free Report: Amazon SageMaker vs. Starburst Enterprise

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