Dataiku vs H2O.ai comparison

 

Comparison Buyer's Guide

Executive Summary
 

Categories and Ranking

Dataiku
Ranking in Data Science Platforms
7th
Average Rating
8.2
Number of Reviews
7
Ranking in other categories
No ranking in other categories
H2O.ai
Ranking in Data Science Platforms
21st
Average Rating
7.6
Number of Reviews
7
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

RK
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.
RK
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.

Quotes from Members

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

Pros

"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."
"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."
"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 is the set of visual data preparation tools."
"If many teams are collaborating and sharing Jupyter notebooks, it's very useful."
"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."
"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."
"The ease of use in connecting to our cluster machines."
"AutoML helps in hands-free initial evaluations of efficiency/accuracy of ML algorithms."
"It is helpful, intuitive, and easy to use. The learning curve is not too steep."
"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."
 

Cons

"I think it would help if Data Science Studio added some more features and improved the data model."
"Although known for Big Data, the processing time to process 1.8 billion records was terribly slow (five days)."
"I find that it is a little slow during use. It takes more time than I would expect for operations to complete."
"Dataiku still needs some coding, and that could be a difference where business data scientists would go for DataRobot more than Dataiku."
"Server up-time needs to be improved. Also, query engines like Spark and Hive need to be more stable."
"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 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."
"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."
"Referring to bullet-3 as well, H2O DataFrame manipulation capabilities are too primitive."
"On the topic of model training and model governance, this solution cannot handle ten or twelve models running at the same time."
"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."
"I would like to see more features related to deployment."
"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."
"The model management features could be improved."
 

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
8%
Computer Software Company
7%
Financial Services Firm
19%
Computer Software Company
11%
Manufacturing Company
9%
Insurance Company
6%
 

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.
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...
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...
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Comparisons

 

Also Known As

Dataiku DSS
No data available
 

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Overview

 

Sample Customers

BGL BNP Paribas, Dentsu Aegis, Link Mobility Group, AramisAuto
poder.io, Stanley Black & Decker, G5, PWC, Comcast, Cisco
Find out what your peers are saying about Dataiku vs. H2O.ai and other solutions. Updated: May 2024.
789,674 professionals have used our research since 2012.