Google Cloud Datalab vs H2O.ai comparison

Google and H2O.ai are both solutions in the Data Science Platforms category. Google is ranked #16 with an average rating of 7.8, while H2O.ai is ranked #20 with an average rating of 7.0. Google holds a 0.9% mindshare in Data Science Platforms, compared to H2O.ai’s 1.5% mindshare. Additionally, 80% of Google users are willing to recommend the solution, compared to 100% of H2O.ai users who would recommend it.

Google Cloud Datalab

Read 6 Google Cloud Datalab reviews

711 Views
612 Comparison Views

80% willing to recommend

H2O.ai

Read 8 H2O.ai reviews

1,209 Views
849 Comparison Views

100% willing to recommend

Google Cloud Datalab

H2O.ai

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Executive SummaryUpdated on Dec 5, 2024

Google Cloud Datalab and H2O.ai are competitors in advanced analytics tools, each offering unique advantages. Google Cloud Datalab takes the lead in cloud integration, while H2O.ai is preferred for machine learning capabilities.

Features: Google Cloud Datalab integrates efficiently with Google BigQuery and Cloud Machine Learning Engine, providing smooth data analysis for Google ecosystem users. Additional features include streamlined infrastructure setup and comprehensive data visualization support. H2O.ai provides a wide range of machine learning algorithms and AutoML capabilities, plus a robust open-source platform for flexible analytics. Its extensive library supports advanced modeling and is ideal for customized analytics solutions.

Room for Improvement: Google Cloud Datalab needs to enhance its machine learning toolset and improve support for non-Google cloud environments. It could benefit from more sophisticated modeling options and better handling of large-scale data sets. H2O.ai should focus on simplifying its user interface and improving integration with non-Java enterprise applications. Improved deployment support and more extensive documentation could enhance user experience.

Ease of Deployment and Customer Service: Google Cloud Datalab offers seamless deployment for Google Cloud users, with responsive support fully integrated into Google’s ecosystem. H2O.ai provides versatile deployment across on-premises and cloud environments, coupled with dedicated support to handle complex machine learning queries efficiently.

Pricing and ROI: Google Cloud Datalab aligns with cloud consumption models, maintaining minimal initial costs favorable to Google users with promising ROI due to integration efficiency. H2O.ai's flexible pricing varies with deployment, possibly incurring higher initial costs due to its comprehensive features. It offers substantial ROI potential for businesses emphasizing advanced ML applications.

To learn more, read our detailed Google Cloud Datalab vs. H2O.ai Report (Updated: March 2025).

Buyer's Guide

Google Cloud Datalab vs. H2O.ai

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

Google Cloud Datalab

Ranking in Data Science Platforms

16th

Average Rating

7.8

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

Data Visualization (18th)

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)

Mindshare comparison

As of April 2025, in the Data Science Platforms category, the mindshare of Google Cloud Datalab is 0.9%, down from 1.2% compared to the previous year. The mindshare of H2O.ai is 1.5%, up from 1.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms

Featured Reviews

Nilesh Gode

Project Manager - Data Science at Mettler-Toledo International Inc.

Easy to setup, stable and easy to design data pipelines

The scalability is average. We have not faced any issues with scalability. There are more than 500 end users using this solution in our company. It is an integral part of the daily operations. The usage pattern is not a one-time thing; employees regularly access and utilize the application. We use it at a global level with a scattered user base. This means that users don't all use the application at the same time. So, around 300 out of 500 employees use the solution, and this usage is spread out throughout the day.

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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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Quotes from Members

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

Pros

"The APIs are valuable."

"In MLOps, when we are designing the data pipeline, the designing of the data pipeline is easy in Google Cloud."

"The infrastructure is highly reliable and efficient, contributing to a positive experience."

"For me, it has been a stable product."

"Google Cloud Datalab is very customizable."

"All of the features of this product are quite good."

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

"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 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."

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

Cons

"Even if your application is always connected to its database, the processing can be cumbersome. It shouldn't be so complicated."

"We have also encountered challenges during our transition period in terms of data control and segmentation. The management of each channel and data structure as it has its own unique characteristics requires very detailed and precise control. The allocation should be appropriate and the complexity increases due to the different time zones and geographic locations of our clients. The process usually involves migrating the existing database sets to gcp and ensure data integrity is maintained. This is the only challenge that we faced while navigating the integers of the solution and honestly it was an interesting and unique experience."

"There is room for improvement in the graphical user interface. So that the initial user would use it properly, that would be a good option."

"Connectivity challenges for end-users, particularly when loading data, environments, and libraries, need to be addressed for an enhanced user experience."

"The interface should be more user-friendly."

"The product must be made more user-friendly."

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

"The model management features could be improved."

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

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

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

"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 and Cost Advice

"It is affordable for us because we have a limited number of users."

"The pricing is quite reasonable, and I would give it a rating of four out of ten."

"The product is cheap."

"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

20%

Computer Software Company

12%

University

11%

Manufacturing Company

Financial Services Firm

20%

Computer Software Company

12%

Manufacturing Company

Educational Organization

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

Questions from the Community

What do you like most about Google Cloud Datalab?

Google Cloud Datalab is very customizable.

See all answers

What needs improvement with Google Cloud Datalab?

Access is always via URL, and unless your network is fast, it would be a little tough in India. In India, if we had a faster network, it would be easier. In a big data environment, like when forcin...

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What is your primary use case for Google Cloud Datalab?

It's for our daily data processing, and there's a batch job that executes it. The process involves more than ten servers or systems. Some of them use a mobile network, some are ONTAP networks, and ...

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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.

See all answers

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 ...

See all answers

Comparisons

IBM SPSS Statistics vs Google Cloud Datalab

Compared 45% of the time

Databricks vs Google Cloud Datalab

Compared 31% of the time

Cloudera Data Science Workbench vs Google Cloud Datalab

Compared 23% of the time

More Google Cloud Datalab Competitors

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

Product Reports

Buyer's Guide

Google Cloud Datalab

April 2025

Download Google Cloud Datalab product report

Buyer's Guide

Data Science Platforms

March 2025

Download H2O.ai product report

Overview

Cloud Datalab is a powerful interactive tool created to explore, analyze, transform and visualize data and build machine learning models on Google Cloud Platform. It runs on Google Compute Engine and connects to multiple cloud services easily so you can focus on your data science tasks.

Google

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

Sample Customers

Information Not Available

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

Buyer's Guide

Google Cloud Datalab vs. H2O.ai

March 2025

Free Report: Google Cloud Datalab vs. H2O.ai

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