Dataiku vs H2O.ai comparison

Dataiku

Read 7 Dataiku reviews

8,856 Views
6,927 Comparison Views

100% willing to recommend

H2O.ai

Read 7 H2O.ai reviews

1,901 Views
1,328 Comparison Views

100% willing to recommend

Dataiku

H2O.ai

Comparison Buyer's Guide

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Executive Summary

We performed a comparison between Dataiku and H2O.ai based on real PeerSpot user reviews.

Find out in this report how the two Data Science Platforms solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

To learn more, read our detailed Dataiku vs. H2O.ai Report (Updated: May 2024).

Buyer's Guide

Dataiku vs. H2O.ai

May 2024

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Helped 787,779 peers since 2012

Categories and Ranking

Dataiku

Ranking in Data Science Platforms

7th

Average Rating

8.2

Number of Reviews

Ranking in other categories

No ranking in other categories

H2O.ai

Ranking in Data Science Platforms

21st

Average Rating

7.6

Number of Reviews

Ranking in other categories

Model Monitoring (8th)

Mindshare comparison

As of June 2024, in the Data Science Platforms category, the mindshare of Dataiku is 9.9%, up from 6.7% compared to the previous year. The mindshare of H2O.ai is 1.1%, down from 1.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms

Unique Categories:

No other categories found

Featured Reviews

RangeshKunnavakkam

Consultant at Netsoft

May 17, 2024

Gives different aspects of modeling approaches and good for multiple teams' collaboration

I used DataRobot. Dataiku has a different kind of structure to it. It's not financially heavy like DataRobot, which caters more to financial companies, like banks. Dataiku doesn't have that yet. I think they are also working on that area. But yeah, there are some key differences between the two products. DataRobot has an additional feature with financial firms that it creates all these financial metrics when you run a time series analysis. Those things I have not seen in Dataiku. If any financial company is choosing between DataRobot and Dataiku, they will definitely go for DataRobot because it creates all these financial metrics. It creates deltas, time series, time difference fields, and things like that. So, that is an added feature that DataRobot has.

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Rahul Koduru

Director of Data Engineering at Transamerica

Dec 11, 2018

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

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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Quotes from Members

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

Pros

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

"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 solution is quite stable."

"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."

"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."

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

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

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

"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."

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

"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."

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

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."

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

"I think it would help if Data Science Studio added some more features and improved the data model."

"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."

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

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

"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."

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

More Dataiku cons

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

"The model management features could be improved."

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

"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."

"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."

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

Pricing and Cost Advice

"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."

"Pricing is pretty steep. Dataiku is also not that 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

18%

Educational Organization

14%

Manufacturing Company

Computer Software Company

Financial Services Firm

19%

Computer Software Company

10%

Manufacturing Company

Insurance 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?

Pricing is pretty steep. Dataiku is also not that cheap. It depends on the client and how much they want to spend towards a tool.

See all answers

What needs improvement with Dataiku Data Science Studio?

The no-code/low-code aspect, where DataRobot doesn't need much coding at all. Dataiku still needs some coding, and that could be a difference where business data scientists would go for DataRobot m...

See all answers

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

My current client has Dataiku. We do sentiment analysis and some small large language models right now. We use Dataiku as a Jupyter Notebook. We use it a lot for marketing and analytics. The market...

See all answers

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Comparisons

Databricks vs Dataiku

Compared 36% of the time

KNIME vs Dataiku

Compared 13% of the time

Alteryx vs Dataiku

Compared 12% of the time

RapidMiner vs Dataiku

Compared 9% of the time

Cloudera Data Science Workbench vs Dataiku

Compared 2% of the time

More Dataiku Competitors

Databricks vs H2O.ai

Compared 20% of the time

Amazon SageMaker vs H2O.ai

Compared 18% of the time

Microsoft Azure Machine Learning Studio vs H2O.ai

Compared 14% of the time

KNIME vs H2O.ai

Compared 12% of the time

IBM Watson Studio vs H2O.ai

Compared 7% of the time

More H2O.ai Competitors

Product Reports

Buyer's Guide

Dataiku

June 2024

Download Dataiku product report

Buyer's Guide

Data Science Platforms

May 2024

Download H2O.ai product report

Also Known As

Dataiku DSS

No data available

Learn More

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

May 2024

Free Report: Dataiku vs. H2O.ai

Find out what your peers are saying about Dataiku vs. H2O.ai and other solutions. Updated: May 2024.

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