Dataiku vs H2O.ai comparison

Dataiku and H2O.ai are both solutions in the Data Science Platforms category. Dataiku is ranked #7 with an average rating of 7.5, while H2O.ai is ranked #20 with an average rating of 7.0. Dataiku holds a 12.1% mindshare in Data Science Platforms, compared to H2O.ai’s 1.5% mindshare. Additionally, 100% of Dataiku users are willing to recommend the solution, compared to 100% of H2O.ai users who would recommend it.

Dataiku

Read 9 Dataiku reviews

7,151 Views
5,935 Comparison Views

100% willing to recommend

H2O.ai

Read 8 H2O.ai reviews

1,366 Views
978 Comparison Views

100% willing to recommend

Dataiku

H2O.ai

Comparison Buyer's Guide

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Executive SummaryUpdated on Dec 5, 2024

Dataiku and H2O.ai are two strong competitors in the AI and machine learning platform market. Dataiku seems to have the upper hand in feature richness and collaboration, while H2O.ai is favored for its advanced AI automation.

Features: Dataiku provides an intuitive environment for data preparation, has comprehensive end-to-end integration options, and excels in collaborative workflows. H2O.ai offers powerful AutoML capabilities, supports advanced algorithms, and provides flexible deployment models.

Room for Improvement: Dataiku could benefit from enhanced AI automation, expanded algorithm options, and better integration with AI-specific tools. H2O.ai needs a more user-friendly interface, improved onboarding support, and better collaboration features for teams.

Ease of Deployment and Customer Service: Dataiku is praised for easy deployment and excellent customer support, making it suitable for integration into existing infrastructures. H2O.ai offers flexible deployment options including cloud and on-premise models but with a potentially steeper learning curve.

Pricing and ROI: Dataiku has a higher initial setup cost but offers long-term ROI through enhanced team productivity and efficient workflows. H2O.ai provides competitive pricing with quick returns on advanced machine learning tasks, making it attractive for organizations focused on fast insights.

To learn more, read our detailed Dataiku vs. H2O.ai Report (Updated: January 2025).

Buyer's Guide

Dataiku vs. H2O.ai

January 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

Dataiku

Ranking in Data Science Platforms

7th

Average Rating

8.0

Reviews Sentiment

7.2

Number of Reviews

Ranking in other categories

No ranking in other categories

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 January 2025, in the Data Science Platforms category, the mindshare of Dataiku is 12.1%, up from 7.8% compared to the previous year. The mindshare of H2O.ai is 1.5%, down from 1.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms

Featured Reviews

Sabrine Bendimerad

Data Scientist at Electricite De France

Saves a lot of time because I can quickly handle all the data preparation tasks and concentrate on building my machine learning algorithms

One of the main challenges was collaboration. Developers typically use GitHub to push and manage code, but integrating GitHub with Dataiku was complicated. While it was theoretically possible to use GitHub with Dataiku, in practice, it was difficult to manage our code effectively and push it from Dataiku to GitHub. Another limitation was its ability to handle different types of data. While Dataiku is powerful for working with structured data, like regular or geospatial data, it struggled with more complex data types such as text and image. In addition to the challenges with GitHub integration, the limited support for diverse data types was another feature lacking at that time.

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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

"If many teams are collaborating and sharing Jupyter notebooks, it's very useful."

"The most valuable feature is the set of visual data preparation tools."

"The most valuable feature of this solution is that it is one tool that can do everything, and you have the ability to very easily push your design to prediction."

"Cloud-based process run helps in not keeping the systems on while processes are running."

"I like the interface, which is probably my favorite part of the solution. It is really user-friendly for an IT person."

"The advantage is that you can focus on machine learning while having access to what they call 'recipes.' These recipes allow me to preprocess and prepare data without writing any code."

"I rate the overall product as eight out of ten."

"Extremely easy to use with its GUI-based functionality and large compatibility with various data sources. Also, maintenance processes are much more automated than ever, with fewer errors."

More Dataiku pros

"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."

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

"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."

"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

"I find that it is a little slow during use. It takes more time than I would expect for operations to complete."

"Server up-time needs to be improved. Also, query engines like Spark and Hive need to be more stable."

"The license is very expensive."

"Dataiku still needs some coding, and that could be a difference where business data scientists would go for DataRobot more than Dataiku."

"One of the main challenges was collaboration. Developers typically use GitHub to push and manage code, but integrating GitHub with Dataiku was complicated."

"Although known for Big Data, the processing time to process 1.8 billion records was terribly slow (five days)."

"There were stability issues: 1) SQL operations, such as partitioning, had bugs and showed wrong results. 2) Due to server downtime, scheduled processes used to fail. 3) Access to project folders was compromised (privacy issue) with wrong people getting access to confidential project folders."

"The license is very expensive. It would be great to have an intermediate license for basic treatments that do not require extensive experience."

More Dataiku cons

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

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

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

"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."

"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."

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

Pricing and Cost Advice

"Pricing is pretty steep. Dataiku is also not that cheap."

"The annual licensing fees are approximately €20 ($22 USD) per key for the basic version and €40 ($44 USD) per key for the version with everything."

"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

18%

Educational Organization

16%

Manufacturing Company

Computer Software Company

Financial Services Firm

21%

Computer Software Company

11%

Manufacturing Company

10%

Energy/Utilities Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

What is your experience regarding pricing and costs for Dataiku Data Science Studio?

It is very expensive.

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What needs improvement with Dataiku Data Science Studio?

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What is your primary use case for Dataiku Data Science Studio?

We use the solution for data science and machine learning.

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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 ...

See all answers

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

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Product Reports

Buyer's Guide

Dataiku

January 2025

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Buyer's Guide

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January 2025

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Also Known As

Dataiku DSS

No data available

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Dataiku

H2O.ai

Overview

Dataiku Data Science Studio is acclaimed for its versatile capabilities in advanced analytics, data preparation, machine learning, and visualization. It streamlines complex data tasks with an intuitive visual interface, supports multiple languages like Python, R, SQL, and scales efficiently for large dataset handling, boosting organizational efficiency and collaboration.

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.

Sample Customers

BGL BNP Paribas, Dentsu Aegis, Link Mobility Group, AramisAuto

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

Buyer's Guide

Dataiku vs. H2O.ai

January 2025

Free Report: Dataiku vs. H2O.ai

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