Dataiku vs Google Cloud Datalab comparison

Dataiku and Google are both solutions in the Data Science Platforms category. Dataiku is ranked #4 with an average rating of 8.0, while Google is ranked #19 with an average rating of 7.5. Dataiku holds a 7.1% mindshare in Data Science Platforms, compared to Google’s 1.5% mindshare. Additionally, 100% of Dataiku users are willing to recommend the solution, compared to 80% of Google users who would recommend it.

Dataiku

Read 17 Dataiku reviews

5,767 Views
5,652 Comparison Views

100% willing to recommend

Google Cloud Datalab

Read 6 Google Cloud Datalab reviews

1,162 Views
802 Comparison Views

80% willing to recommend

Dataiku

Google Cloud Datalab

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Executive SummaryUpdated on Dec 5, 2024

Google Cloud Datalab and Dataiku are products in the data analytics domain. Dataiku holds the advantage with its comprehensive features and user-friendly analytics tools.

Features: Google Cloud Datalab offers seamless integration with Google Cloud services, rapid notebook-based exploration, and real-time collaboration. Dataiku provides robust data preparation tools, machine learning capabilities, and easy-to-use analytics supporting teamwork.

Room for Improvement: Google Cloud Datalab could enhance its advanced analytics tools, improve user support for complex setups, and expand its non-Google Cloud integrations. Dataiku could benefit from reducing its initial setup complexity, offering more detailed customization options, and optimizing system resource usage for large datasets.

Ease of Deployment and Customer Service: Google Cloud Datalab ensures straightforward deployment in Google's ecosystem, leveraging Google's support. Dataiku offers flexible deployment on-premises or in the cloud with personalized support, facilitating a smoother transition.

Pricing and ROI: Google Cloud Datalab's integration lowers setup costs, appealing to budget-conscious users. Dataiku, despite higher initial costs, offers substantial ROI with its impactful features, justifying its pricing through efficiency in complex data tasks.

To learn more, read our detailed Dataiku vs. Google Cloud Datalab Report (Updated: December 2025).

Buyer's Guide

Dataiku vs. Google Cloud Datalab

December 2025

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Helped 881,707 peers since 2012

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

Google Cloud Datalab

Ranking in Data Science Platforms

19th

Average Rating

7.8

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

Data Visualization (17th)

Mindshare comparison

As of February 2026, in the Data Science Platforms category, the mindshare of Dataiku is 7.1%, down from 12.4% compared to the previous year. The mindshare of Google Cloud Datalab is 1.5%, up from 0.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms Market Share Distribution
Product	Market Share (%)
Dataiku	7.1%
Google Cloud Datalab	1.5%
Other	91.4%

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.

Read full review

LijomonJose

System Architect at UST Global EspaÃ±a

dashboards are good and data visualization is more meaningful for the end-user

Access is always via URL, and unless your network is fast, it would be a little tough in India. In India, if we had a faster network, it would be easier. In a big data environment, like when forcing your database with over a billion records, it can be tough for the end-user to manage the data. You need to have a single entity system in each environment. It's not because of GCP, but it would be great to have options like MongoDB or other similar tools in GCP. Then, we wouldn't always need to connect to the cloud and execute SQL queries. Even if your application is always connected to its database, the processing can be cumbersome. It shouldn't be so complicated. Once the data is collected, it should be easily sorted.

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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

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

"The solution is quite stable."

"I consider the return on investment with Dataiku valuable because for us, it is one single platform where all our data scientists come together and work on any model building, so it is collaboration, plus having everything in one place, organized, having proper project management, and then built-in capabilities which help to facilitate model building."

"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."

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

"I believe the return on investment looks positive."

"One of the valuable features of Dataiku is the workflow capability."

More Dataiku pros

"In MLOps, when we are designing the data pipeline, the designing of the data pipeline is easy in Google Cloud."

"The APIs are valuable."

"The infrastructure is highly reliable and efficient, contributing to a positive experience."

"For me, it has been a stable product."

"All of the features of this product are quite good."

"Google Cloud Datalab is very customizable."

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)."

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

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

"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."

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

"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."

More Dataiku cons

"The product must be made more user-friendly."

"There is room for improvement in the graphical user interface. So that the initial user would use it properly, that would be a good option."

"Connectivity challenges for end-users, particularly when loading data, environments, and libraries, need to be addressed for an enhanced user experience."

"The interface should be more user-friendly."

"We have also encountered challenges during our transition period in terms of data control and segmentation. The management of each channel and data structure as it has its own unique characteristics requires very detailed and precise control. The allocation should be appropriate and the complexity increases due to the different time zones and geographic locations of our clients. The process usually involves migrating the existing database sets to gcp and ensure data integrity is maintained. This is the only challenge that we faced while navigating the integers of the solution and honestly it was an interesting and unique experience."

"Even if your application is always connected to its database, the processing can be cumbersome. It shouldn't be so complicated."

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."

"The pricing is quite reasonable, and I would give it a rating of four out of ten."

"The product is cheap."

"It is affordable for us because we have a limited number of users."

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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

24%

Computer Software Company

10%

University

Comms Service Provider

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

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

No data available

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.

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 do you like most about Google Cloud Datalab?

Google Cloud Datalab is very customizable.

See all answers

What needs improvement with Google Cloud Datalab?

See all answers

What is your primary use case for Google Cloud Datalab?

It's for our daily data processing, and there's a batch job that executes it. The process involves more than ten servers or systems. Some of them use a mobile network, some are ONTAP networks, and ...

See all answers

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Product Reports

Buyer's Guide

Dataiku

February 2026

Download Dataiku product report

Buyer's Guide

Google Cloud Datalab

January 2026

Download Google Cloud Datalab product report

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

Cloud Datalab is a powerful interactive tool created to explore, analyze, transform and visualize data and build machine learning models on Google Cloud Platform. It runs on Google Compute Engine and connects to multiple cloud services easily so you can focus on your data science tasks.

Google

Sample Customers

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

Information Not Available

Buyer's Guide

Dataiku vs. Google Cloud Datalab

December 2025

Free Report: Dataiku vs. Google Cloud Datalab

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