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Domino Data Science Platform vs H2O.ai 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

Domino Data Science Platform
Ranking in Data Science Platforms
15th
Average Rating
7.6
Reviews Sentiment
6.7
Number of Reviews
2
Ranking in other categories
No ranking in other categories
H2O.ai
Ranking in Data Science Platforms
16th
Average Rating
7.6
Reviews Sentiment
6.8
Number of Reviews
10
Ranking in other categories
Model Monitoring (4th)
 

Mindshare comparison

As of August 2025, in the Data Science Platforms category, the mindshare of Domino Data Science Platform is 2.7%, down from 2.9% compared to the previous year. The mindshare of H2O.ai is 1.8%, up from 1.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Science Platforms Market Share Distribution
ProductMarket Share (%)
Domino Data Science Platform2.7%
H2O.ai1.8%
Other95.5%
Data Science Platforms
 

Featured Reviews

AS
Accelerated machine learning model development with seamless deployment
We used Domino Data Science Platform for developing and working with machine learning models. It facilitated end-to-end development processes. Domino is based on Git, enabling collaboration similar to using Git. Each user operates on their own equivalent of a branch or fork, and once finished, they…
Abhay Vyas - PeerSpot reviewer
Advanced model selection and time efficiency meet needs but documentation and fusion model support are needed
Even though H2O.ai provides the best model, there could be improvements in certain areas. For instance, when you want to work with fusion models, H2O.ai doesn't provide that kind of information. Currently, it provides individual models as outcomes. If it could offer combinations of models, such as suggesting using XGBoost along with SVM for wonderful results, that fusion model concept would be a good option for developers. I hope the fusion model concept will be implemented soon in H2O.ai. Regarding documentation, I faced challenges as I didn't see much information from a documentation perspective. When I was trying to learn how to train and test H2O.ai, there was limited documentation available. If they could improve in that area, it would be really beneficial.

Quotes from Members

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

Pros

"The workspaces, which are like wrappers of Docker containers, made it easy to start development environments using Domino."
"The scalability of the solution is good; I'd rate it four out of five."
"The most valuable feature of H2O.ai is that it is plug-and-play."
"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."
"The ease of use in connecting to our cluster machines."
"I have utilized the AutoML feature in H2O.ai, which is one of the very powerful features where you don't need to worry about which algorithm is best for your model."
"AutoML helps in hands-free initial evaluations of efficiency/accuracy of ML algorithms."
"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 features are the machine learning tools, the support for Jupyter Notebooks, and the collaboration that allows you to share it across people."
 

Cons

"The deployment of large language models (LLMs) could be improved."
"The predictive analysis feature needs improvement."
"It lacks the data manipulation capabilities of R and Pandas DataFrames. We would kill for dplyr offloading H2O."
"Referring to bullet-3 as well, H2O DataFrame manipulation capabilities are too primitive."
"H2O.ai can improve in areas like multimodal support and prompt engineering."
"On the topic of model training and model governance, this solution cannot handle ten or twelve models running at the same time."
"It needs a drag and drop GUI like KNIME, for easy access to and visibility of workflows."
"One improvement I would like to see in H2O.ai is regarding the integration capabilities with different data sources, as I've seen platforms like DataIQ and DataBricks offer great integration with various data sources."
"The model management features could be improved."
"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."
 

Pricing and Cost Advice

Information not available
"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."
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Top Industries

By visitors reading reviews
Financial Services Firm
39%
Manufacturing Company
10%
Insurance Company
8%
Computer Software Company
6%
Computer Software Company
16%
Financial Services Firm
16%
Manufacturing Company
9%
Educational Organization
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
By reviewers
Company SizeCount
Small Business2
Midsize Enterprise3
Large Enterprise7
 

Questions from the Community

What needs improvement with Domino Data Science Platform?
The deployment of large language models (LLMs) could be improved. Currently, Domino provides a simple server that cannot handle big deployments, which is not suitable for LLMs.
What is your primary use case for Domino Data Science Platform?
We used Domino Data Science Platform for developing and working with machine learning models. It facilitated end-to-end development processes. Domino is based on Git, enabling collaboration similar...
What advice do you have for others considering Domino Data Science Platform?
It's important to have a DevOps team well-versed with cloud-native solutions to manage Domino effectively. Relying solely on data scientists might not be sufficient. I'd rate the solution eight out...
What needs improvement with H2O.ai?
Even though H2O.ai provides the best model, there could be improvements in certain areas. For instance, when you want to work with fusion models, H2O.ai doesn't provide that kind of information. Cu...
What is your primary use case for H2O.ai?
I used H2O.ai on several POCs for my previous company, and it helped me find the best model. I needed to determine which model was performing better for job portal data. At that time, H2O.ai was ev...
What advice do you have for others considering H2O.ai?
For larger datasets, model computation or model training and testing typically takes considerable time because with individual models, you need to train and test each one. With H2O.ai, these concer...
 

Also Known As

Domino Data Lab Platform
No data available
 

Interactive Demo

Demo not available
 

Overview

 

Sample Customers

Allstate, GSK, AstraZeneca, Federal Reserve, US Navy, Bristol Myers Squibb, Bayer, BNP Paribas, Moodys, New York Life
poder.io, Stanley Black & Decker, G5, PWC, Comcast, Cisco
Find out what your peers are saying about Domino Data Science Platform vs. H2O.ai and other solutions. Updated: July 2025.
866,218 professionals have used our research since 2012.