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Dataiku vs Saturn Cloud comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 5, 2024

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
8.2
Reviews Sentiment
6.5
Number of Reviews
20
Ranking in other categories
No ranking in other categories
Saturn Cloud
Ranking in Data Science Platforms
16th
Average Rating
10.0
Reviews Sentiment
7.5
Number of Reviews
6
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of March 2026, in the Data Science Platforms category, the mindshare of Dataiku is 6.7%, down from 12.5% compared to the previous year. The mindshare of Saturn Cloud is 1.0%, up from 0.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Science Platforms Mindshare Distribution
ProductMindshare (%)
Dataiku6.7%
Saturn Cloud1.0%
Other92.3%
Data Science Platforms
 

Featured Reviews

SK
Senior Data Scientist at Deloitte
Visual workflows have streamlined healthcare analytics and have reduced reporting time significantly
In terms of improvement, I cannot comment on the LLMs or the agentic view as I have not used them yet. However, I feel that better documentation is necessary. Dataiku should establish a stronger community since this is proprietary software, where users can share knowledge. Although they have some community interaction, it is often challenging to find assistance when stuck. For example, when I was new to Dataiku and trying to use an external optimization tool such as CPLEX, I struggled with resource directory linking to a project's notebook. Detailed documentation and community discussions could have significantly alleviated these issues for users such as myself.
Filip Stefanovski - PeerSpot reviewer
Works at a tech consulting company with 51-200 employees
Easy to use with good performance and collaborative features
My main suggestion for improvement centers on pricing. Introducing a tier modelled after AWS spot instances would be a game-changer. Users could bid on unused compute capacity, potentially leading to significant cost savings during off-peak hours or for less time-critical tasks. Spot instances empower users with tighter budgets or fluctuating workloads to strategically leverage lower-cost resources for development, experimentation, and background tasks. This frees up on-demand instances for truly time-sensitive work.

Quotes from Members

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

Pros

"Dataiku is really a very intuitive platform that allows you to carry out data projects from end to end, with the opportunity to reuse templates, models, and recipes, which is one of the big advantages of using it."
"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."
"Cloud-based process run helps in not keeping the systems on while processes are running."
"If many teams are collaborating and sharing Jupyter notebooks, it's very useful."
"The best features Dataiku offers that help me with my demand forecasting and data science projects include having a complete overview of the flow directly from the flowchart, allowing me to observe all the steps in a single overview, and the ability to use a no-code, low-code node."
"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."
"Traceability is vital since I manage many cohorts, and collaboration is key as I have multiple engineers substituting for one another."
"Dataiku has positively impacted my organization, specifically in one project where we performed migration from AWS to Dataiku, speeding up the solution by close to 40% and reducing architecture costs by almost 70%, which was a significant benefit and greatly impacted our operations."
"It offered an excellent development environment while not touching our production cloud resources."
"Saturn Cloud supports GPU as part of the environment, which is essential for many computational tasks in machine learning projects. It also allows us to edit the environment, including the image, before we start the cloud resources. This feature lets us quickly set up the environment without the hassle of moving the data and code to another cloud device."
"The feature I like the most about Saturn Cloud is that it has lightning-fast CPUs."
"They provide a centralized space for data, code, and results."
"There is plenty of computational resources (both GPU, CPU and disk space)."
"It didn't take long to see that Saturn Cloud could scale with my needs, providing more resources when required."
 

Cons

"The technical support from Dataiku is not good. The support team does not provide adequate assistance, and there are concerns about billing requests."
"I think it would help if Data Science Studio added some more features and improved the data model."
"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."
"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."
"There is room for improvement in terms of allowing for more code-based features."
"I find that it is a little slow during use. It takes more time than I would expect for operations to complete."
"Maybe on the interface in general, the information can easily get lost."
"Public Clouds integration and sandbox environments would be a true game changer."
"It would be nice to have more hardware category options, like TPU coprocessors or ARM64 CPUs."
"My main suggestion for improvement centers on pricing. Introducing a tier modelled after AWS spot instances would be a game-changer."
"We'd like to have the capability for installing more libraries."
"Providing more detailed and beginner-friendly documentation, especially for advanced features, could greatly enhance the user experience."
"Saturn Cloud should include prebuilt images for advanced data science packages like LightGBM in the next release. If possible, they should also provide a Kaggle image, which contains the most common Python packages used in machine learning."
 

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."
Information not available
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Top Industries

By visitors reading reviews
Financial Services Firm
18%
Manufacturing Company
10%
Computer Software Company
9%
Energy/Utilities Company
6%
No data available
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business6
Midsize Enterprise2
Large Enterprise13
By reviewers
Company SizeCount
Small Business4
Midsize Enterprise1
Large Enterprise3
 

Questions from the Community

What is your experience regarding pricing and costs for Dataiku Data Science Studio?
I am not the person involved in the process regarding pricing, setup cost, and licensing.
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...
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...
What do you like most about Saturn Cloud?
There is plenty of computational resources (both GPU, CPU and disk space).
What needs improvement with Saturn Cloud?
My main suggestion for improvement centers on pricing. Introducing a tier modelled after AWS spot instances would be a game-changer. Users could bid on unused compute capacity, potentially leading ...
What is your primary use case for Saturn Cloud?
I'm leveraging a cloud-based platform for competitive machine learning. Tight deadlines and resource-intensive models demand powerful hardware. The cloud provides scalable GPUs and RAM, letting me ...
 

Comparisons

 

Also Known As

Dataiku DSS
No data available
 

Overview

 

Sample Customers

BGL BNP Paribas, Dentsu Aegis, Link Mobility Group, AramisAuto
Nvidia, Snowflake, Kaggle, Faeth, Advantest, Stanford University, Senseye and more.
Find out what your peers are saying about Dataiku vs. Saturn Cloud and other solutions. Updated: March 2026.
884,873 professionals have used our research since 2012.