Dataiku vs Google Cloud Datalab comparison

Dataiku and Google are both solutions in the Data Science Platforms category. Dataiku is ranked #2 with an average rating of 8.2, while Google is ranked #22. Dataiku holds a 4.8% mindshare in Data Science Platforms, compared to Google’s 1.7% mindshare. Additionally, 100% of Dataiku users are willing to recommend the solution, compared to 80% of Google users who would recommend it.

Dataiku

Read 21 Dataiku reviews

7,664 Views
7,511 Comparison Views

100% willing to recommend

Google Cloud Datalab

Read 6 Google Cloud Datalab reviews

2,400 Views
1,416 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: June 2026).

Buyer's Guide

Dataiku vs. Google Cloud Datalab

June 2026

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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

2nd

Average Rating

8.2

Reviews Sentiment

6.5

Number of Reviews

Ranking in other categories

No ranking in other categories

Google Cloud Datalab

Ranking in Data Science Platforms

22nd

Average Rating

7.8

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

Data Visualization (26th)

Mindshare comparison

As of August 2026, in the Data Science Platforms category, the mindshare of Dataiku is 4.8%, down from 13.0% compared to the previous year. The mindshare of Google Cloud Datalab is 1.7%, up from 1.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms Mindshare Distribution
Product	Mindshare (%)
Dataiku	4.8%
Google Cloud Datalab	1.7%
Other	93.5%

Data Science Platforms

Featured Reviews

Shubhamkumar Gomar

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.

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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 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."

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

"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 as most of our projects have been migrated to Dataiku, and now people are relying on it as a go-to tool for all our data use cases."

"Using Dataiku has meant that we spend less time on preparing and cleaning data, and we spend less time on blending models together, ultimately meaning that we can spend more time modeling."

"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."

"Our clients can easily drag and drop components and use them on the spot."

"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."

More Dataiku pros

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

"The APIs are valuable."

"Google Cloud Datalab is very customizable."

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

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

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

Cons

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

"We still encounter some integration issues."

"Dataiku is down a lot of times, and we have to wait for sometimes five, ten, or fifteen minutes, after which it gets working again, and during those times, we are unable to get our work done."

"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."

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

"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."

"Maybe on the interface in general, the information can easily get lost."

"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."

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."

"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."

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

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

"The interface should be more user-friendly."

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."

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

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

"The product is cheap."

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Top Industries

By visitors reading reviews

Financial Services Firm

18%

Manufacturing Company

10%

Computer Software Company

Energy/Utilities Company

Construction Company

20%

Financial Services Firm

13%

Outsourcing Company

University

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	6
Midsize Enterprise	2
Large Enterprise	13

No data available

Questions from the Community

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

The licenses are a bit high for companies that are still hesitating to get started with using Dataiku. For my personal projects, I used the thirty-day free trial. Regarding my company, I did not ha...

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What needs improvement with Dataiku Data Science Studio?

I have no suggestions for improvements because it's all good; it just sometimes lags a lot, and I don't know if the server is full or what, but it sometimes takes a lot of time while loading and re...

See all answers

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

My main use case for Dataiku involves ETL pipelines, mainly for data analysis, and I majorly use SQL queries for that. For ETL pipelines and data analysis, I had to create the output by combining a...

See all answers

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Product Reports

Buyer's Guide

Dataiku

August 2026

Download Dataiku product report

Buyer's Guide

Google Cloud Datalab

July 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

Google Cloud Datalab offers an integrated environment for seamless data processing and analysis. It combines robust infrastructure with free call-up features to enhance user experience, making it a go-to choice for data-driven tasks.

Google Cloud Datalab is geared towards users seeking efficient data handling solutions. It provides a seamless setup with robust infrastructure, focusing on enhancing APIs and offering meaningful data visualization through its dashboards. Notable AI capabilities include auto-completion and data logging, although some minor configuration challenges exist. While transitioning from AWS can be complex, the platform supports dynamic data pipeline design that suits Python development, offering an end-user friendly environment.

What are the key features of Google Cloud Datalab?

Robust Infrastructure: Provides seamless setup with strong backend support.
Free Call-Up: Allows use without extra costs, enhancing accessibility.
Data Visualization: Dashboards offer meaningful insights into data trends.
AI Capabilities: Features auto-completion and data logging for efficiency.
Customizable Architecture: Adapts to different data handling needs effectively.

What benefits should users expect from Google Cloud Datalab?

Efficient Data Analysis: Streamlines exploratory data analysis in machine learning.
Enhanced API Value: API integrations add significant value to data handling.
Friendly Python Environment: Suitable for end-users looking for ease in Python development.
Adaptive Pipeline Design: Supports complex data transitions from AWS.
Global Availability: Helps overcome time lag issues with server location selection.

In specific industries, Google Cloud Datalab is instrumental in managing data analysis, machine learning exploration, and dataset preprocessing. It facilitates the transfer of workloads from AWS and ensures efficient daily data processing. Organizations benefit from its capability to provision machine learning models into Vertex AI, bolstering research and development efforts. The global availability feature plays a significant role in selecting optimal server locations, addressing time lag and connectivity challenges.

Google

Sample Customers

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

Information Not Available

Buyer's Guide

Dataiku vs. Google Cloud Datalab

June 2026

Free Report: Dataiku vs. Google Cloud Datalab

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