H2O.ai vs Starburst Enterprise comparison

H2O.ai and Starburst are both solutions in the Data Science Platforms category. H2O.ai is ranked #20 with an average rating of 7.0, while Starburst is ranked #14 with an average rating of 8.5. H2O.ai holds a 1.5% mindshare in Data Science Platforms, compared to Starburst’s 2.2% mindshare. Additionally, 100% of H2O.ai users are willing to recommend the solution, compared to 100% of Starburst users who would recommend it.

H2O.ai

Read 8 H2O.ai reviews

1,209 Views
849 Comparison Views

100% willing to recommend

Starburst Enterprise

Read 2 Starburst Enterprise reviews

1,176 Views
1,011 Comparison Views

100% willing to recommend

H2O.ai

Starburst Enterprise

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Executive SummaryUpdated on Dec 5, 2024

H2O.ai and Starburst Enterprise compete in the data analytics space. H2O.ai appears stronger in affordability and support, while Starburst Enterprise leads in advanced features, justifying its higher cost.

Features: H2O.ai offers robust machine learning, easy integration, and strong predictive analytics. Starburst Enterprise features an advanced query engine, compatibility across multiple data sources, and high scalability.

Ease of Deployment and Customer Service: H2O.ai provides straightforward deployment and comprehensive support appealing to those seeking simplicity. Starburst Enterprise’s more complex deployment is balanced by detailed documentation and a dedicated support team offering detailed customization options.

Pricing and ROI: H2O.ai is cost-effective, providing high ROI due to lower setup costs and efficient performance. Starburst Enterprise involves a higher initial cost, but its extensive features promise a significant ROI for enterprises needing advanced capabilities.

To learn more, read our detailed H2O.ai vs. Starburst Enterprise Report (Updated: March 2025).

Buyer's Guide

H2O.ai vs. Starburst Enterprise

March 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

H2O.ai

Ranking in Data Science Platforms

20th

Average Rating

7.6

Reviews Sentiment

7.2

Number of Reviews

Ranking in other categories

Model Monitoring (6th)

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 April 2025, in the Data Science Platforms category, the mindshare of H2O.ai is 1.5%, up from 1.5% compared to the previous year. The mindshare of Starburst Enterprise is 2.2%, up from 1.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms

Featured Reviews

Kashif Yaseen

Trainee Decision Scientist at a tech services company with 1,001-5,000 employees

Plug-and-play convenience enhances productivity but needs better multimodal support

We mostly used the solution in the domain that I'm working. We had most of the use cases around chatbots and conversational BI The solution was plug-and-play, meaning most of the components were handled by the solution itself rather than building them from scratch. This was useful for our banking…

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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

"AutoML helps in hands-free initial evaluations of efficiency/accuracy of ML algorithms."

"The most valuable features are the machine learning tools, the support for Jupyter Notebooks, and the collaboration that allows you to share it across people."

"The ease of use in connecting to our cluster machines."

"It is helpful, intuitive, and easy to use. The learning curve is not too steep."

"Fast training, memory-efficient DataFrame manipulation, well-documented, easy-to-use algorithms, ability to integrate with enterprise Java apps (through POJO/MOJO) are the main reasons why we switched from Spark to H2O."

"One of the most interesting features of the product is their driverless component. The driverless component allows you to test several different algorithms along with navigating you through choosing the best algorithm."

"The most valuable feature of H2O.ai is that it is plug-and-play."

"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

"It lacks the data manipulation capabilities of R and Pandas DataFrames. We would kill for dplyr offloading H2O."

"I would like to see more features related to deployment."

"The interpretability module has room for improvement. Also, it needs to improve its ability to integrate with other systems, like SageMaker, and the overall integration capability."

"The model management features could be improved."

"It needs a drag and drop GUI like KNIME, for easy access to and visibility of workflows."

"H2O.ai can improve in areas like multimodal support and prompt engineering."

"Referring to bullet-3 as well, H2O DataFrame manipulation capabilities are too primitive."

"On the topic of model training and model governance, this solution cannot handle ten or twelve models running at the same time."

"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

"We have seen significant ROI where we were able to use the product in certain key projects and could automate a lot of processes. We were even able to reduce staff."

"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

20%

Computer Software Company

12%

Manufacturing Company

Educational Organization

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

What needs improvement with H2O.ai?

H2O.ai can improve in areas like multimodal support and prompt engineering. They are already working on updates and changes. Although I haven't explored all the new products they've added to their ...

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What is your primary use case for H2O.ai?

We mostly used the solution in the domain that I'm working. We had most of the use cases around chatbots and conversational BI.

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What advice do you have for others considering H2O.ai?

It is important to address data privacy concerns and ensure you're choosing the right vendor that meets your use case demands. Also, you may leave my name, Kashif, but please keep the company name ...

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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...

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What needs improvement with Starburst Enterprise?

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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...

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Comparisons

Dataiku vs H2O.ai

Compared 22% of the time

Databricks vs H2O.ai

Compared 19% of the time

Amazon SageMaker vs H2O.ai

Compared 19% of the time

Microsoft Azure Machine Learning Studio vs H2O.ai

Compared 16% of the time

KNIME vs H2O.ai

Compared 10% of the time

More H2O.ai Competitors

Databricks vs Starburst Enterprise

Compared 42% of the time

Starburst Galaxy vs Starburst Enterprise

Compared 17% of the time

Dremio vs Starburst Enterprise

Compared 16% of the time

Alteryx vs Starburst Enterprise

Compared 10% of the time

Apache Spark Streaming vs Starburst Enterprise

Compared 4% of the time

More Starburst Enterprise Competitors

Product Reports

Buyer's Guide

Data Science Platforms

March 2025

Download H2O.ai product report

Buyer's Guide

Data Science Platforms

March 2025

Download Starburst Enterprise product report

Overview

H2O is a fully open source, distributed in-memory machine learning platform with linear scalability. H2O’s supports the most widely used statistical & machine learning algorithms including gradient boosted machines, generalized linear models, deep learning and more. H2O also has an industry leading AutoML functionality that automatically runs through all the algorithms and their hyperparameters to produce a leaderboard of the best models. The H2O platform is used by over 14,000 organizations globally and is extremely popular in both the R & Python communities.

H2O.ai

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

poder.io, Stanley Black & Decker, G5, PWC, Comcast, Cisco

Information Not Available

Buyer's Guide

H2O.ai vs. Starburst Enterprise

March 2025

Free Report: H2O.ai vs. Starburst Enterprise

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