IBM Watson Studio vs Starburst Galaxy comparison

IBM and Starburst are both solutions in the Data Science Platforms category. IBM is ranked #17 with an average rating of 7.3, while Starburst is ranked #6 with an average rating of 9.5. IBM holds a 2.2% mindshare in Data Science Platforms, compared to Starburst’s 1.5% mindshare. Additionally, 100% of IBM users are willing to recommend the solution, compared to 100% of Starburst users who would recommend it.

IBM Watson Studio

Read 19 IBM Watson Studio reviews

3,415 Views
2,254 Comparison Views

100% willing to recommend

Starburst Galaxy

Read 11 Starburst Galaxy reviews

1,681 Views
1,328 Comparison Views

100% willing to recommend

IBM Watson Studio

Starburst Galaxy

Comparison Buyer's Guide

Download the report

Executive Summary

We performed a comparison between IBM Watson Studio 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 IBM Watson Studio vs. Starburst Galaxy Report (Updated: June 2026).

Buyer's Guide

IBM Watson Studio vs. Starburst Galaxy

June 2026

Download the complete report

Helped 900,747 peers since 2012

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

IBM Watson Studio

Ranking in Data Science Platforms

17th

Average Rating

8.0

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

AI Development Platforms (16th)

Starburst Galaxy

Ranking in Data Science Platforms

6th

Average Rating

9.4

Reviews Sentiment

2.5

Number of Reviews

Ranking in other categories

Streaming Analytics (8th)

Mindshare comparison

As of June 2026, in the Data Science Platforms category, the mindshare of IBM Watson Studio is 2.2%, up from 2.1% compared to the previous year. The mindshare of Starburst Galaxy is 1.5%, up from 0.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms Mindshare Distribution
Product	Mindshare (%)
Starburst Galaxy	1.5%
IBM Watson Studio	2.2%
Other	96.3%

Data Science Platforms

Featured Reviews

reviewer2715654

Director and Marketing Consultant at a non-tech company with 1-10 employees

Collaborative analytics workspace has improved campaign insights and saves weekly manual effort

One of the best features IBM Watson Studio offers is the ability to collaborate across teams using a centralized workspace. The centralized workspace helps my team collaborate because we did not need to spend excessive time on manual processes. This helped us collaborate across teams by selecting which data and which channels should be reflected in IBM Watson Studio. In this way, we saved time and could easily see campaign outcomes and make better data-driven marketing decisions. IBM Watson Studio has positively impacted my organization by being time-efficient and enabling collaboration, as we can see everything in one screen. It helped improve our efficiency and provided deeper customer insights that enable better decision-making. It definitely helped our weekly time efficiency by saving manual workload because we have a lot of work going on. It really helped us in analyzing the data and analytics.

Read full review

Niketan Kumar

Advisory Solutions Architect at Dell Technologies

Unified data querying has accelerated petabyte-scale analytics and simplified dashboard delivery

Starburst Galaxy offers me several best features, which include very fast querying results, automatic indexing of data for long tables, a cost-based optimizer which reduces the time to query large tables, and an agentic feature that lets me talk to my data.I find myself relying most on querying from different databases as well as automatic indexing in my day-to-day work, as I am a data science architect who needs to get the queries in a very short period of time. Starburst Galaxy serves the best purpose for me because if my SLAs are not met with my customers, they will raise a case, and I have tried many other tools, but Starburst Galaxy fits the best. Starburst Galaxy has positively impacted my organization since we were struggling with Denodo and Dremio, which had their own features but were not helpful in querying large amounts of data, especially semi-structured or unstructured data. Starburst Galaxy addresses this with many YAML files and manifest files for automated maintenance, and it helps reduce the small file problem in different HDFS systems. Additionally, Starburst Galaxy has an MCP server that connects to various agentic pipelines, reducing the time to market for data consumption.

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

"My advice to anybody who is considering this solution is that it is really good for an enterprise-level organization."

"It stands out for its substantial AI capabilities, offering a broad spectrum of features for crafting solutions that meet specific requirements."

"The solution was a deployed model and I was just installing the API, sending some data and returning some data from REST API, and it was very easy to use."

"The solution is very easy to use."

"Watson Studio is the most complete tool for AI projects."

"In my experience, AutoML is the most valuable feature of IBM Watson Studio."

"IBM Watson Studio has positively impacted my organization by being time-efficient and enabling collaboration, as we can see everything in one screen."

"The best features in IBM Watson Studio for me personally are moving away from the alarm dictionary or moving away from the rule-based alarms to more the AI Ops portion where you have IBM Watson Studio with some of the machine learning to do the correlations and learning seasonality, et cetera."

More IBM Watson Studio pros

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

"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."

"I am now able to answer questions in a couple of minutes that would otherwise take hours or days of time for my data engineering teams."

"Starburst Galaxy serves the best purpose for me because if my SLAs are not met with my customers, they will raise a case, and I have tried many other tools, but Starburst Galaxy fits the best."

"Starburst Galaxy has significantly improved our data architecture flexibility and performance management by solving cross-database query challenges and enabling us to utilize iceberg tables externally across our entire data ecosystem."

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

"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."

More Starburst Galaxy pros

Cons

"More features in data virtualization would be helpful. The solution could use an interactive dashboard that could make exploration easier."

"The product is already really great but for most researchers or a person like me, there are few templates to try something new, so we're limited."

"I think maybe the support is an area where it lacks."

"I assess the flexibility of IBM Watson Studio in integrating with open-source machine learning tools and frameworks, and I find that it is not always that easy, but with the PMRs, they normally help you quite quickly to solve it."

"IBM Watson Studio has great features but the decision making in their decision making feature is less good than other options."

"Initially, it was quite complex. For us, it was not only a matter of getting it installed, that was just a start. It was also trying to come up with a standard way of implementing it across the entire organization, which had been a challenge."

"The main challenge lies in visibility and ease of use."

"We would like to see it less as one big, massive product, but more based on smaller services that we can then roll out to consumers."

More IBM Watson Studio cons

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

"Starburst Galaxy can be improved by discovering unstructured data and building in streaming ingestion because we are currently using Kafka for that purpose."

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

"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."

"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."

"I think there are areas of improvement with respect to AI adaptability, and also in general, the amount of connectors working with other tools are areas where it can be expanded."

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

Pricing and Cost Advice

"IBM Watson Studio is a reasonably priced product"

"IBM Watson Studio is an expensive solution."

"The pricing is generally reasonable and straightforward but can vary significantly depending on the specific workloads in use."

"Watson Studio's pricing is reasonable for what you get."

Information not available

See which vendors are best for you

Use our free recommendation engine to learn which Data Science Platforms solutions are best for your needs.

See recommendations

900,747 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

14%

Manufacturing Company

10%

Construction Company

University

Financial Services Firm

29%

Computer Software Company

12%

University

Construction Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	14
Midsize Enterprise	2
Large Enterprise	12

By reviewers
Company Size	Count
Small Business	4
Midsize Enterprise	2
Large Enterprise	3

Questions from the Community

What is your experience regarding pricing and costs for IBM Watson Studio?

The pricing for IBM Watson Studio is very high, but we are talking about an enterprise solution. Most of the time we try to convince the customer with the price because it is a robust and enterpris...

See all answers

What needs improvement with IBM Watson Studio?

I have not used the AutoAI feature yet, if it is a feature in IBM Watson Studio. I think the user experience of IBM Watson Studio can be improved, as I am trying to use other products outside IBM a...

See all answers

What is your primary use case for IBM Watson Studio?

IBM Watson Studio is used primarily with our customers, though we have also tested it in our company and laboratories. I am also dealing with products like IBM Watson Studio and IBM Cognos.

See all answers

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

I recommend experimenting with different cluster sizes to determine what works best for your particular use case.

See all answers

What needs improvement with Starburst Galaxy?

Starburst Galaxy can be improved by discovering unstructured data and building in streaming ingestion because we are currently using Kafka for that purpose. We rely on third-party tools for ingesti...

See all answers

What is your primary use case for Starburst Galaxy?

My main use case for Starburst Galaxy is querying petabytes of data across vast data sources, and I use a federated query engine to join data sources from different databases and then join them usi...

See all answers

Comparisons

Microsoft Azure Machine Learning Studio vs IBM Watson Studio

Compared 6% of the time

Databricks vs IBM Watson Studio

Compared 5% of the time

Amazon SageMaker vs IBM Watson Studio

Compared 5% of the time

Azure OpenAI vs IBM Watson Studio

Compared 4% of the time

KNIME Business Hub vs IBM Watson Studio

Compared 4% of the time

More IBM Watson Studio Competitors

Starburst Enterprise vs Starburst Galaxy

Compared 22% of the time

Databricks vs Starburst Galaxy

Compared 10% of the time

Dremio vs Starburst Galaxy

Compared 6% of the time

Qlik Talend Cloud vs Starburst Galaxy

Compared 6% of the time

Domino Data Science Platform vs Starburst Galaxy

Compared 3% of the time

More Starburst Galaxy Competitors

Product Reports

Buyer's Guide

IBM Watson Studio

June 2026

Download IBM Watson Studio product report

Buyer's Guide

Starburst Galaxy

May 2026

Download Starburst Galaxy product report

Also Known As

Watson Studio, IBM Data Science Experience, Data Science Experience, DSx

No data available

Overview

IBM Watson Studio offers comprehensive support for machine learning lifecycles with a focus on collaboration and automation, integrating open-source tools for ease of use by developers and data scientists.

IBM Watson Studio provides end-to-end management of machine learning processes, supporting tasks from data validation to model deployment and API integration. Its integration with Jupyter Notebook is highly regarded, allowing seamless development and deployment of machine learning models. Users benefit from flexible machine-learning frameworks and strong visual tools that enhance productivity, with multi-cloud support further boosting efficiency. Despite some concerns about interface complexity and responsiveness with large datasets, Watson Studio remains a cost-effective, time-saving solution for predictive analytics and algorithm development.

What are Watson Studio's Key Features?

AutoAI and AutoML: Automates model building and deployment, saving time.
Open-source Integration: Supports popular tools like Jupyter Notebook for smoother workflows.
Seamless Deployment: Strong capabilities for deploying models across various environments.
Flexible Frameworks: Supports a wide range of machine-learning frameworks.
Multi-cloud Support: Enhances deployment and operational efficiency.

What Benefits and ROI can Users Expect?

Cost-effectiveness: Provides a budget-friendly way to manage machine learning projects.
Time Efficiency: Streamlined processes and automation reduce execution time.
Enhanced Cash Management: Predictive models assist in cash flow analysis.
User Experience: Improved data insights lead to better customer experiences.

IBM Watson Studio is implemented across industries for tasks like marketing analytics, chatbot development, and AI-driven data studies. It aids in data cleansing and algorithm development, including radar sensor applications, optimizing decision-making and enhancing experiences in fields such as operations data analysis and predictive analytics.

IBM

Starburst Galaxy offers rapid query speeds and robust cluster management, enhancing data engineering efficiency while supporting AWS integrations and cross-database functionality. Users benefit from its advanced data integration and federated querying capabilities.

Starburst Galaxy stands out with a compute-focused architecture that excels in facilitating seamless data integration. Technological innovations like autoscaling clusters and automated metadata management optimize operations in multi-tenant environments. With a keen emphasis on compatibility, the platform provides support for AWS Glue and enables federated querying across S3, Snowflake, and Redshift. This adaptability ensures comprehensive ETL processes and enhances analytics through querying SQL Server, Google Sheets, and blob stores. While noted for its robust capabilities, users seek improvements in cluster startup times, Tableau and AI support, and desire infrastructure-as-code enhancements.

What are Starburst Galaxy's key features?

Rapid Query Speeds: Enhances data processing with quick responses.
Cluster Autoscaling: Optimizes resource allocation for varying workloads.
Federated Querying: Cross-database functionality for seamless integration.
Compute-Focused Architecture: Ensures efficiency and scalability.
Automated Metadata Management: Streamlines data handling processes.
AWS Integration: Broad compatibility with AWS services.

What benefits should users evaluate in reviews?

Data Engineering Efficiency: Reduces operating expenses and improves productivity.
Cost Reduction: Highlights lower storage costs with efficient data handling.
Seamless Integration: Enables robust data integration across various platforms.
Cluster Management: Supports diverse workloads with advanced management tools.

In industries focusing on large-scale data efforts, Starburst Galaxy plays an essential role in connecting data sources like Amazon S3 and RDS, streamlining tasks in data engineering and ad-hoc analysis across complex environments. Teams leverage its cross-database querying to boost AWS analytics, with features tailored for sectors needing agile data solutions, from ETL pipelines to secure data federation.

Starburst

Sample Customers

GroupM, Accenture, Fifth Third Bank

Information Not Available

Buyer's Guide

IBM Watson Studio vs. Starburst Galaxy

June 2026

Free Report: IBM Watson Studio vs. Starburst Galaxy

Find out what your peers are saying about IBM Watson Studio vs. Starburst Galaxy and other solutions. Updated: June 2026.

DOWNLOAD NOW

900,747 professionals have used our research since 2012.

See our IBM Watson Studio vs. Starburst Galaxy report.

See our list of best Data Science Platforms vendors.

We monitor all Data Science Platforms reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.