H2O.ai Reviews

Name: H2O.ai
Brand: H2O.ai
Rating: 3.8 (8 reviews)

3.8 out of 5

8 reviews
100% willing to recommend

132 followers

What is H2O.ai?

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.

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H2O.ai is the #5 ranked solution in top Model Monitoring solutions and #20 ranked solution in top Data Science Platforms. PeerSpot users give H2O.ai an average rating of 7.6 out of 10. H2O.ai is most commonly compared to Databricks: H2O.ai vs Databricks. H2O.ai is popular among the large enterprise segment, accounting for 73% of users researching this solution on PeerSpot. The top industry researching this solution are professionals from a financial services firm, accounting for 22% of all views.

Buyer's Guide

Data Science Platforms

February 2025

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Featured H2O.ai reviews

Kashif Yaseen

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

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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Rahul Koduru

Director of Data Engineering at Transamerica

One example, we are able to automate life insurance. We have to underwrite policies. When somebody applies for a policy, we take their blood, then assign them a risk: substandard, standard, preferred, etc. Depending on this, we price our products. Usually the process is that you take the blood, then it goes to a lab and we get the lab results back, then an underwriter takes a look at the lab results. This is usually done in a two week time frame to get a rating. We were able to build models to automate all of this, and now, it happens in real-time. Somebody can apply online and get issued a policy right away.

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MvpOfMac4841

Managing VP of Machine Learning at a financial services firm with 10,001+ employees

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. I would like more support for scalability and deep learning. Right now, they are very strong in supervise and supervise learning, but not in deep learning. I'd like to see them be more well-rounded, where they have support for deep learning, but I'm not sure that is their business model.

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H2O.ai mindshare

Product category:

As of March 2025, the mindshare of H2O.ai in the Data Science Platforms category stands at 1.5%, down from 1.5% compared to the previous year, according to calculations based on PeerSpot user engagement data.

Data Science Platforms

Key learnings from peers

Valuable Features

H2O.ai is praised for fast training, memory-efficient DataFrame manipulation, and well-documented algorithms. Users appreciate AutoML for hands-free evaluations and intuitive driverless capabilities for algorithm testing and interpretability. Integration with enterprise Java apps and cluster connectivity ease are highlighted. Support for Jupyter Notebooks and collaboration features enhance usability, alongside plug-and-play attributes that reduce development needs. The learning curve is described as manageable and tools intuitive to use.

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

Room for Improvement

H2O.ai faces challenges in enhancing DataFrame manipulation, providing a user-friendly drag-and-drop GUI, and expanding deployment features. The interpretability module and integration with systems like SageMaker require advancements. Scalability and deep learning support are limited. Users point out issues with model management, feature engineering, and managing multiple model operations simultaneously. Improved Python integration is desired, along with enhancements in multimodal support and prompt engineering, areas that are reportedly under development.

"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."
"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

"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."

Popular Use Cases

Organizations utilize H2O.ai primarily for real-time predictive analytics, fraud prevention, and data science. It supports large-scale model building and machine learning through Java-based feature engineering pipelines and integration with R and Python. Their use includes developing models for logistics forecasting, machine learning model development, and deploying models with optimized hyperparameters. The platform runs efficiently on various environments, including lightweight VMs and Ubuntu laptops, enabling under 1ms latency for transaction scoring.

Service and Support

H2O.ai offers good customer service with available and exceptional support. Users mention the technical support is excellent, rating it an eight out of ten, although some solve issues independently or rely on community forums. Others haven't needed to contact technical support due to the application's clarity or personal problem-solving. A smaller customer base allows H2O.ai to provide available assistance when required, as noted by users who have met with H2O staff for support.

Deployment

H2O.ai's initial setup is described as straightforward and accessible via pip, JAR download, or R install.packages. Users mention it is easy to get started, with integration and configuration receiving positive feedback. Some rate the setup as an eight out of ten, while others give it a nine. Deployment requires two to two and a half hours, even without expert assistance, indicating simplicity in the process.

Scalability

H2O.ai demonstrates effective scalability with smooth operations for large use cases and massive database environments. Users report satisfactory scalability, accommodating infrastructure-dependent scaling needs. Many large corporations find it fits their requirements, though some with evolving AI applications express concerns about handling diverse use cases.

Stability

Users encountered a few bugs with H2O.ai and opened JIRA tickets for them. They managed to find their own workarounds. During prototype testing, they didn't heavily stress-test it. Users noted that they are in the early phases of evaluation. Many haven't applied significant stress yet but also reported no stability issues so far.

These insights are based on the in-depth reviews provided by peers to help you make a better buying decision.

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