Dataiku vs H2O.ai comparison

Dataiku and H2O.ai are both solutions in the Data Science Platforms category. Dataiku is ranked #4 with an average rating of 8.3, while H2O.ai is ranked #15 with an average rating of 7.7. Dataiku holds a 8.0% mindshare in Data Science Platforms, compared to H2O.ai’s 1.9% 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 17 Dataiku reviews

5,603 Views
5,491 Comparison Views

100% willing to recommend

H2O.ai

Read 10 H2O.ai reviews

1,331 Views
1,238 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: December 2025).

Buyer's Guide

Dataiku vs. H2O.ai

December 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

4th

Average Rating

8.0

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

No ranking in other categories

H2O.ai

Ranking in Data Science Platforms

15th

Average Rating

7.6

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

Model Monitoring (4th)

Mindshare comparison

As of January 2026, in the Data Science Platforms category, the mindshare of Dataiku is 8.0%, down from 12.1% compared to the previous year. The mindshare of H2O.ai is 1.9%, up from 1.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms Market Share Distribution
Product	Market Share (%)
Dataiku	8.0%
H2O.ai	1.9%
Other	90.1%

Data Science Platforms

Featured Reviews

PriyankaSharma3

Cdao/Global Head Of Data And Analytics at Givaudan Roure

Unified platform has accelerated model development and improved collaborative data science work

I think Dataiku could be improved or enhanced in future releases with more 'talk to my data' capabilities, maybe more NLP features, and maybe a platform to build agents. These improvements would benefit me and my processes because they will help us to continue using Dataiku as one platform; right now we are exploring other platforms for the features which are missing, and if they are available within the same platform, I think it will increase the usage of Dataiku further. I think the pricing and licensing of Dataiku is a bit expensive; it could be improved further, and I think they should have a different kind of licensing model as well.

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Muhammad Adnan

Senior Manager - AI at Shamal Holding

Have improved machine learning model automation and reduced decision-making time

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. H2O.ai could benefit from enhanced integration with real-time versus offline data sources, as well as improvements in productionalization solutions, including better deployment options on platforms like Azure and CI/CD integration. One of the features I'd like to see included in upcoming releases of H2O.ai pertains to the growing trend of Generative AI, with applications for LLM-based models and vector databases. I would like to see a solution similar to Azure AI Foundry, which provides the flexibility to integrate different LLMs into applications, including H2O-GPT and other models for varied applications.

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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

"Data Science Studio's data science model is very useful."

"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."

"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."

"Dataiku is highly regarded as it is a leader in the Gartner ranking."

"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."

"The solution is quite stable."

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

"The best features Dataiku offers include the ability for users to use the node without having to code and the functionality related to low-code/no-code."

More Dataiku pros

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

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

"H2O.ai provides better flexibility where I could examine more models and obtain results, and based on these results, I could make the next set of decisions."

"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."

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

More H2O.ai pros

Cons

"The interface for the web app can be a bit difficult. It needs to have better capabilities, at least for developers who like to code. This is due to the fact that everything is enabled in a single window with different tabs. For them to actually develop and do the concurrent testing that needs to be done, it takes a bit of time. That is one improvement that I would like to see - from a web app developer perspective."

"The ability to have charts right from the explorer would be an improvement."

"I have not seen a return on investment with Dataiku in terms of time saved, money saved, or fewer employees needed."

"In the next release of this solution, I would like to see deep learning better integrated into the tool and not simply an extension or plugin."

"We still encounter some integration issues."

"There is room for improvement in terms of allowing for more code-based features."

"In terms of enhancing collaboration within my team, I would not say Dataiku is the best one because it's so expensive."

"One area for improvement is the need for more capabilities similar to those provided by NVIDIA for parallel machine learning training. We still encounter some integration issues."

More Dataiku cons

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

"The model management features could be improved."

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

"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."

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

"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."

More H2O.ai cons

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

17%

Computer Software Company

Manufacturing Company

Energy/Utilities Company

Financial Services Firm

15%

Computer Software Company

11%

Educational Organization

Manufacturing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	4
Midsize Enterprise	2
Large Enterprise	11

By reviewers
Company Size	Count
Small Business	2
Midsize Enterprise	3
Large Enterprise	7

Questions from the Community

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

I find the pricing of Dataiku quite affordable for our customers, as they are usually large companies. However, it is a pricey solution and I primarily recommend it to bigger companies.

See all answers

What needs improvement with Dataiku Data Science Studio?

To improve Dataiku, it could enhance its visualization features, as it is not possible in Dataiku to create direct visualizations or to integrate a web app directly or in a simpler way as it is pos...

See all answers

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

My main use case for Dataiku is for data science and AI projects. I use Dataiku for a demand forecasting use case where the objective is to predict the demand for each product for the next four mon...

See all answers

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...

See all answers

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...

See all answers

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...

See all answers

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Dremio vs Dataiku

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Altair RapidMiner vs H2O.ai

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Product Reports

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Dataiku

January 2026

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Also Known As

Dataiku DSS

No data available

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.

Dataiku

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

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

December 2025

Free Report: Dataiku vs. H2O.ai

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