Dataiku Customer Reviews 2025 | Machine Learning Platforms

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Dataiku

Composite Score

7.8 /10

CX Score

8.0 /10

29 Reviews Write a Review

What is Dataiku?

Dataiku is the platform democratizing access to data and enabling enterprises to build their own path to AI in a human-centric way. With Dataiku, everyone can get involved in data and AI projects on a single platform for design and production that delivers use cases in days, not months. No matter where they sit, they work in a safe and governed way that helps manage risk and create trust to drive high-quality outputs and value for your business.

Company Details

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Awards & Recognition

Dataiku won the following awards in the Machine Learning Platforms category

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2 Top Rated Capabilities

6 Top Rated Features

Latest Award, 2025

Strategy and Innovation
Service Experience
Product Impact
Conflict Resolution

2024

5 Top Rated Capabilities

8 Top Rated Features

2024

2023 Emotional Footprint Champion

2023

Strategy and Innovation
Service Experience
Product Impact
Contract Negotiation
Conflict Resolution

2023

2023 Data Quadrant Champion

2023

4 Top Rated Capabilities

3 Top Rated Features

2023

2022 Emotional Footprint Champion

2022

Strategy and Innovation
Service Experience
Product Impact
Contract Negotiation
Conflict Resolution

2022

2022 Data Quadrant Champion

2022

4 Top Rated Capabilities

6 Top Rated Features

2022

Dataiku Ratings

Real user data aggregated to summarize the product performance and customer experience.
Download the entire Product Scorecard to access more information on Dataiku.

Product scores listed below represent current data. This may be different from data contained in reports and awards, which express data as of their publication date.

86 Likeliness to Recommend

1
Since last award

97 Plan to Renew

76 Satisfaction of Cost Relative to Value

2
Since last award

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Emotional Footprint Overview

Product scores listed below represent current data. This may be different from data contained in reports and awards, which express data as of their publication date.

+96 Net Emotional Footprint

The emotional sentiment held by end users of the software based on their experience with the vendor. Responses are captured on an eight-point scale.

How much do users love Dataiku?

0% Negative

3% Neutral

97% Positive

Pros

Helps Innovate
Continually Improving Product
Reliable
Performance Enhancing

Feature Ratings

Average 84

Data Pre-Processing

Openness and Flexibility

Data Exploration and Visualization

Feature Engineering

Data Ingestion

Model Training

Ensembling

Algorithm Recommendation

Algorithm Diversity

Explainability

Data Labeling

Vendor Capability Ratings

Average 80

Vendor Support

Ease of Data Integration

Usability and Intuitiveness

Breadth of Features

Quality of Features

Business Value Created

Availability and Quality of Training

Ease of Implementation

Ease of Customization

Ease of IT Administration

Product Strategy and Rate of Improvement

Also Featured in...

Data Preparation for Analytics

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Dataiku Reviews

Celio O.

Role: Public Sector
Industry: Government
Involvement: IT Leader or Manager

Validated Review

Verified Reviewer

Submitted Oct 2022

Great User interface but limited features for deep

Likeliness to Recommend

9 /10

What differentiates Dataiku from other similar products?

User friendly

What is your favorite aspect of this product?

User Interface

What do you dislike most about this product?

Model tuning

What recommendations would you give to someone considering this product?

Drag and drop

Pros

Trustworthy
Client Friendly Policies
Fair
Altruistic

Sergey M.

Role: Consultant
Industry: Insurance
Involvement: IT Leader or Manager

Validated Review

Verified Reviewer

Submitted Oct 2022

Good product, but pricy for what it can do

Likeliness to Recommend

7 /10

What differentiates Dataiku from other similar products?

Ease of conducting analytical work on big data and building Machine Learning models using out-of-the-box algorithms

What is your favorite aspect of this product?

It's great for one-off analytical projects

What do you dislike most about this product?

It doesn't follow or enforce best practices for software development and DevOps / MLOps, which results in projects that are non-reproducible and/or very hard to maintain in the long term.

What recommendations would you give to someone considering this product?

Clearly understand what your organisation's real needs are. If it's one-off analytical projects or simple predictive models, Dataiku can be a great choice (assuming you can afford the license). If, however, you need to build custom Machine Learning models and want do to this following the best practices from software engineering and DevOps / MLOps, you may be disappointed by choosing this product.

Pros

Efficient Service
Effective Service
Caring
Respectful

Yanchen D.

Role: Sales Marketing
Industry: Finance
Involvement: End User of Application

Validated Review

Verified Reviewer

Submitted Oct 2022

Great products, keep up the good work!

Likeliness to Recommend

8 /10

What differentiates Dataiku from other similar products?

Dataiku is my daily tool for machine learning model development and maintenance. It’s super easy to integrate our database into the platform and further clean the data for building models. Dataiku provides a one-stop-shop for ML.

What is your favorite aspect of this product?

One-stop-shop for ML from pulling data to model deployment

What do you dislike most about this product?

Team collaboration feature could be better

What recommendations would you give to someone considering this product?

Great product with lots of features we need

Pros

Continually Improving Product
Acts with Integrity
Helps Innovate
Reliable

Latest Research

Dataiku

What is Dataiku?

Company Details

Need Assistance?

Awards & Recognition

2023 Emotional Footprint Champion

2023 Data Quadrant Champion

2022 Emotional Footprint Champion

2022 Data Quadrant Champion

Dataiku Ratings

86 Likeliness to Recommend

1 Since last award

97 Plan to Renew

76 Satisfaction of Cost Relative to Value

2 Since last award

Emotional Footprint Overview

+96 Net Emotional Footprint

How much do users love Dataiku?

Pros

Feature Ratings Average rating of all product features, some of which may not be shown below

Data Pre-Processing Ability to wrangle or pre-process data to ready it for model training.

Openness and Flexibility Integration with open-source and 3P resources (languages, frameworks, libraries, etc.)

Data Exploration and Visualization Ability to visually manipulate, interact with, and explore data, and perform basic descriptive statistics and pattern detection.

Feature Engineering Ability to extract and build features from raw data.

Data Ingestion Ability to access data from various sources on-prem and in the cloud and of various type: structured, unstructured, and semi-structured, real-time, etc.

Model Training Ability to train a model with a range of data sets that you provide.

Ensembling 'Ability to combine ML models for best results.

Algorithm Recommendation Ability to recommend a best ML algorithm to use based on dataset characteristics.

Algorithm Diversity Provides multiple diverse ready-to-use machine learning algorithms out-of-the-box.

Explainability Ability to explain/ visualize the model.

Data Labeling Ability to annotate/ label data to create a training data set.

Vendor Capability Ratings Average rating of all vendor capabilities, some of which may not be shown below

Vendor Support The importance of vendor support will vary for each organization depending on internal capabilities, but there will always be issues that only the vendor can resolve.

Ease of Data Integration Use this data to determine whether the product will cause headaches or make data integration easy.

Usability and Intuitiveness End user learning curves cost the organization money. Pay attention to your end users' technical ability to determine how important UX is in your purchase.

Breadth of Features Users prefer feature rich software that enables them to perform diverse series of tasks. This data expresses user satisfaction with the product's breadth of features.

Quality of Features Feature quality is just as important as quantity. Use this data to determine if this product will do what you're purchasing it to do, easily, intuitively, reliably, and effectively.

Business Value Created Software needs to create value for employees, customers, partners, and, ultimately, shareholders. This data expresses user satisfaction - or lack thereof - with the product's business value.

Availability and Quality of Training Effective and readily available training enables users to get the most out of the software you've chosen. Use this section to make sure your vendor's training programs and materials measure up.

Ease of Implementation Successfully implementing new software is necessary to realize its full value and promote end user adoption. This data indicates whether or not the product is easy to implement.

Ease of Customization Don't get bogged down in a difficult customization; use this data to make sure you can easily achieve the functionality you need for your particular situation.

Ease of IT Administration This data indicates whether IT personnel will be able to resolve issues and perform configurations efficiently and effectively.

Product Strategy and Rate of Improvement Vendors who don't stay on top of emerging needs and trends won't enable you to meet your business goals. Use this data to separate innovators from imposters.

Also Featured in...

Data Preparation for Analytics

Dataiku Reviews

Celio O.

Great User interface but limited features for deep

Likeliness to Recommend

Pros

Sergey M.

Good product, but pricy for what it can do

Likeliness to Recommend

Pros

Yanchen D.

Great products, keep up the good work!

Likeliness to Recommend

Pros

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Title

1
Since last award

2
Since last award

Feature Ratings

Data Pre-Processing

Openness and Flexibility

Data Exploration and Visualization

Feature Engineering

Data Ingestion

Model Training

Ensembling

Algorithm Recommendation

Algorithm Diversity

Explainability

Data Labeling

Vendor Capability Ratings

Vendor Support

Ease of Data Integration

Usability and Intuitiveness

Breadth of Features

Quality of Features

Business Value Created

Availability and Quality of Training

Ease of Implementation

Ease of Customization

Ease of IT Administration

Product Strategy and Rate of Improvement