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Amazon SageMaker vs Starburst Enterprise comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 5, 2024

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
38
Ranking in other categories
AI Development Platforms (4th)
Starburst Enterprise
Ranking in Data Science Platforms
13th
Average Rating
8.6
Reviews Sentiment
6.9
Number of Reviews
2
Ranking in other categories
Streaming Analytics (16th)
 

Mindshare comparison

As of February 2026, in the Data Science Platforms category, the mindshare of Amazon SageMaker is 4.3%, down from 7.6% compared to the previous year. The mindshare of Starburst Enterprise is 1.6%, down from 2.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Science Platforms Market Share Distribution
ProductMarket Share (%)
Amazon SageMaker4.3%
Starburst Enterprise1.6%
Other94.1%
Data Science Platforms
 

Featured Reviews

Saurabh Jaiswal - PeerSpot reviewer
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.
KamleshPant - PeerSpot reviewer
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.

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"The intuitive interface and streamlined user experience make it easy to navigate and set up various tools like Visual Studio Code or Jupyter Notebook."
"The tool makes our ML model development a bit more efficient because everything is in one environment."
"The tangible benefits we have observed from using Amazon SageMaker include improved time to insight and generally the common stack that is easier to support over time."
"We've had no problems with SageMaker's stability."
"The feature I found most valuable is the data catalog, as it assists with the lineage of data through the preparation pipeline."
"One of the most valuable features of Amazon SageMaker for me is the one-touch deployment, which simplifies the process greatly."
"SageMaker offers functionalities like Jupyter Notebooks for development, built-in algorithms, model tuning, and options to deploy models on managed infrastructure."
"The solution's ability to improve work at my organization stems from the ensemble model and a combination of various models it provides."
"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

"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."
"Scalability to handle big data can be improved by making integration with networks such as Hadoop and Apache Spark easier."
"The solution is complex to use."
"While integration is available, there are concerns about how secure this integration is, particularly when exposing data to SageMaker."
"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."
"They could add features such as managing environments, experiment management across those environments, and the integration with training datasets as you go through those experiments."
"The main challenge with Amazon SageMaker is the integrations."
"I would recommend having more walkthrough videos and articles beyond AWS Skill Builder."
"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

"On average, customers pay about $300,000 USD per month."
"Databricks solution is less costly than Amazon SageMaker."
"The pricing is comparable."
"The pricing could be better, especially for querying. The per-query model feels expensive."
"SageMaker is worth the money for our use case."
"The tool's pricing is reasonable."
"I would rate the solution's price a ten out of ten since it is very high."
"The solution is relatively cheaper."
"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%
Computer Software Company
10%
Manufacturing Company
9%
University
6%
Financial Services Firm
37%
Computer Software Company
7%
Government
4%
Manufacturing Company
4%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business12
Midsize Enterprise11
Large Enterprise17
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?
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.
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...
What needs improvement with Starburst Enterprise?
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 ...
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...
 

Also Known As

AWS SageMaker, SageMaker
No data available
 

Overview

 

Sample Customers

DigitalGlobe, Thomson Reuters Center for AI and Cognitive Computing, Hotels.com, GE Healthcare, Tinder, Intuit
Information Not Available
Find out what your peers are saying about Amazon SageMaker vs. Starburst Enterprise and other solutions. Updated: December 2025.
881,733 professionals have used our research since 2012.