Compare Dataiku and IBM Cloud Pak for Data 
Featured Products
At a Glance
Market Segments
Enterprise (61.5% of reviews)
Enterprise (50.0% of reviews)
Entry-Level Pricing
Free 3 Users
No pricing available
Dataiku
Star Rating
(196)4.4 out of 5
Market Segments
Enterprise (61.5% of reviews)
Pros & Cons
Entry-Level Pricing
Free 3 Users
Free Trial is available
Browse all 3 pricing plansIBM Cloud Pak for Data
Star Rating
(88)4.3 out of 5
Market Segments
Enterprise (50.0% of reviews)
Pros & Cons
Not enough data
Entry-Level Pricing
No pricing available
Learn more about IBM Cloud Pak for DataSummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- G2 reviewers report that Dataiku excels in user experience, with many praising its intuitive graphical user interface that simplifies managing code and datasets visually. Users appreciate how everything is automatically aligned, making the platform feel very soothing to use.
- According to verified reviews, Dataiku's implementation process is notably quick and user-friendly, allowing teams to rapidly prototype and launch their MVPs. This ease of use is a significant advantage for organizations looking to accelerate their AI integration.
- Users say that IBM Cloud Pak for Data plays a crucial role in digital transformation, offering a comprehensive integrated approach to data management and analytics. This capability helps organizations unify siloed data, which is invaluable for identifying trends and optimizing resources.
- Reviewers mention that while IBM Cloud Pak for Data has an intuitive user interface, it may not be as seamless as Dataiku for certain workflows. Some users have highlighted challenges in navigating the platform compared to the more straightforward experience offered by Dataiku.
- G2 reviewers highlight that Dataiku has a higher overall satisfaction score, reflecting its strong performance across various metrics, including ease of setup and quality of support. Users frequently commend the platform for its plug-and-play capabilities, which significantly speed up the data analysis process.
- According to recent feedback, while IBM Cloud Pak for Data integrates well with Watson AI services, some users feel that it lacks the same level of flexibility and ease of use that Dataiku provides, particularly for teams looking to quickly adapt and iterate on their data projects.
Pricing
Entry-Level Pricing
Dataiku
Install Dataiku on Your Infrastructure
Free
3 Users
Download Free Edition on Mac, Linux, or virtual machine | Collaborate with up to 3 users | Prepare data and build basic data projects and apps (no deployment, automation, governance)
- Visual Data Prep & AutoML
- Full Code (Python, R) Data Prep & ML
- The LLM Mesh
IBM Cloud Pak for Data
No pricing availableFree Trial
Dataiku
Free Trial is available
IBM Cloud Pak for Data
No trial information availableRatings
Meets Requirements
8.7
175
8.5
47
Ease of Use
8.8
174
8.1
47
Ease of Setup
8.1
135
7.2
26
Ease of Admin
8.0
61
7.6
27
Quality of Support
8.6
159
8.3
42
Has the product been a good partner in doing business?
8.6
61
8.1
25
Product Direction (% positive)
9.1
172
8.8
47
Features by Category
Reports
7.8
76
Not enough data
7.7
75
Not enough data
7.5
76
Not enough data
7.5
74
Not enough data
7.6
76
Not enough data
Data Preparation
8.7
65
Not enough data
8.2
63
Not enough data
Data Modeling and Blending
8.6
62
Not enough data
8.6
62
Not enough data
8.7
62
Not enough data
Generative AI
7.9
51
Not enough data
8.0
50
Not enough data
Agentic AI - Analytics Platforms
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Self Service
8.2
76
Not enough data
8.6
75
Not enough data
8.4
75
Not enough data
7.9
74
Not enough data
8.7
74
Not enough data
8.1
74
Not enough data
Deployment & Integration - Analytics Platforms
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Advanced Analytics
8.4
74
Not enough data
7.8
76
Not enough data
8.0
74
Not enough data
Performance & Scalability - Analytics Platforms
Not enough data
Not enough data
Not enough data
Not enough data
Advanced Analytics & Modeling - Analytics Platforms
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Agentic AI Capabilities - Analytics Platforms
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Personalized Intelligence - Analytics Platforms
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Building Reports
8.5
92
Not enough data
8.6
89
Not enough data
7.7
85
Not enough data
8.1
89
Not enough data
Customization
Not enough data
8.0
5
Not enough data
Not enough data
Not enough data
Not enough data
Infrastructure
Not enough data
Not enough data
Not enough data
9.0
5
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Management
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Deployment
8.1
15
Not enough data
8.7
15
Not enough data
8.6
15
Not enough data
8.9
15
Not enough data
9.0
15
Not enough data
Deployment
7.8
12
Not enough data
8.5
13
Not enough data
8.5
13
Not enough data
8.8
13
Not enough data
8.8
13
Not enough data
Management
7.6
13
Not enough data
8.5
13
Not enough data
8.3
13
Not enough data
7.8
13
Not enough data
Operations
8.5
13
Not enough data
8.3
13
Not enough data
8.8
13
Not enough data
Management
7.5
12
Not enough data
8.5
13
Not enough data
7.9
13
Not enough data
Generative AI
8.3
6
Not enough data
8.3
6
Not enough data
Infrastructure Provision
Not enough data
7.7
8
Not enough data
8.1
7
Not enough data
7.9
7
Not enough data
8.3
6
Not enough data
8.5
8
Not enough data
7.9
8
Not enough data
8.3
8
Not enough data
8.3
7
Management
Not enough data
8.6
7
Not enough data
8.3
7
Not enough data
8.1
8
Functionality
Not enough data
8.3
8
System
8.6
75
9.5
11
Model Development
8.4
78
8.3
8
8.8
78
8.5
8
8.6
78
9.1
9
8.5
77
8.8
8
Model Development
8.5
78
8.5
12
Machine/Deep Learning Services
8.0
74
8.7
9
8.1
74
9.4
8
7.9
73
9.6
8
7.9
72
9.0
7
Machine/Deep Learning Services
7.7
68
9.2
8
8.0
70
9.3
7
Deployment
8.1
74
9.3
9
8.2
77
9.3
9
8.3
76
8.9
9
Generative AI
8.1
60
8.3
5
8.0
60
8.3
5
7.7
59
8.3
5
Agentic AI - Data Science and Machine Learning Platforms
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Data Management
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
8.3
5
Analytics
Not enough data
Not enough data
Security
Not enough data
9.0
5
Not enough data
Not enough data
Not enough data
Not enough data
Generative AI
Not enough data
Not enough data
Not enough data
Not enough data
Agentic AI - Data Fabric
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Responses
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Automation - AI Agents
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Platform
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Autonomy - AI Agents
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Generative AI
Not enough data
Not enough data
Not enough data
Not enough data
Scalability and Performance - Generative AI Infrastructure
8.1
18
Not enough data
7.6
19
Not enough data
7.5
16
Not enough data
Cost and Efficiency - Generative AI Infrastructure
7.0
17
Not enough data
7.6
9
Not enough data
8.1
9
Not enough data
Integration and Extensibility - Generative AI Infrastructure
7.5
17
Not enough data
7.7
18
Not enough data
7.4
16
Not enough data
Security and Compliance - Generative AI Infrastructure
7.3
16
Not enough data
7.4
16
Not enough data
7.7
16
Not enough data
Usability and Support - Generative AI Infrastructure
7.1
18
Not enough data
7.1
16
Not enough data
Integration - Machine Learning
8.7
26
Not enough data
Learning - Machine Learning
8.3
27
Not enough data
7.9
28
Not enough data
8.0
28
Not enough data
Large Language Model Operationalization (LLMOps)Hide 15 FeaturesShow 15 Features
7.5
11
Not enough data
Prompt Engineering - Large Language Model Operationalization (LLMOps)
8.3
11
Not enough data
7.9
11
Not enough data
Inference Optimization - Large Language Model Operationalization (LLMOps)
7.1
11
Not enough data
Model Garden - Large Language Model Operationalization (LLMOps)
7.7
11
Not enough data
Custom Training - Large Language Model Operationalization (LLMOps)
7.6
11
Not enough data
Application Development - Large Language Model Operationalization (LLMOps)
7.9
11
Not enough data
Model Deployment - Large Language Model Operationalization (LLMOps)
8.5
11
Not enough data
7.7
11
Not enough data
Guardrails - Large Language Model Operationalization (LLMOps)
6.8
11
Not enough data
6.7
11
Not enough data
Model Monitoring - Large Language Model Operationalization (LLMOps)
7.3
11
Not enough data
7.4
11
Not enough data
Security - Large Language Model Operationalization (LLMOps)
7.6
11
Not enough data
7.4
11
Not enough data
Gateways & Routers - Large Language Model Operationalization (LLMOps)
7.1
11
Not enough data
Agentic AI - AWS Marketplace
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Workflow Design & Integration - AI Orchestration
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Performance Optimization & Analytics - AI Orchestration
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Governance & Compliance Controls - AI Orchestration
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Data Ingestion & Preparation - Low-Code Machine Learning Platforms
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Model Construction & Automation - Low-Code Machine Learning Platforms
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Statistical Tool
8.2
35
8.3
5
8.5
35
8.8
8
8.0
35
8.6
7
Data Analysis
8.5
37
8.7
9
8.8
37
9.0
7
Decision Making
8.6
35
8.3
6
7.9
36
8.8
7
7.8
35
8.8
8
8.6
36
8.3
8
Generative AI
8.6
20
Not enough data
8.3
20
Not enough data
Data Transformation
8.5
44
8.5
27
|
Verified
9.2
39
9.1
15
|
Verified
Connectivity
8.2
38
8.0
23
|
Verified
8.3
40
8.6
22
|
Verified
8.7
41
8.1
25
|
Verified
8.9
44
8.7
24
|
Verified
Operations
8.1
42
8.7
26
|
Verified
9.1
41
8.9
25
|
Verified
8.7
40
8.4
23
|
Verified
8.6
40
8.8
24
|
Verified
8.9
35
8.7
13
|
Verified
Agentic AI - AI Agents
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Building Reports
8.5
92
Not enough data
8.6
89
Not enough data
7.7
85
Not enough data
8.1
89
Not enough data
Platform
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Categories
Categories
Shared Categories
Dataiku and IBM Cloud Pak for Data are categorized as Generative AI Infrastructure, Big Data Analytics, Data Science and Machine Learning Platforms, and Predictive Analytics
Unique Categories
IBM Cloud Pak for Data is categorized as Data Fabric, Virtual Private Cloud (VPC), and Infrastructure as a Service (IaaS)
Reviews
Reviewers' Company Size
Dataiku
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
IBM Cloud Pak for Data
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
Small-Business
(50 or fewer emp.)
Mid-Market
(51-1000 emp.)
Enterprise
(> 1000 emp.)
Reviewers' Industry
Financial Services
15.0%
Pharmaceuticals
7.2%
Manufacturing
5.6%
Retail
5.6%
Consulting
5.0%
Other
61.7%
Computer Software
12.9%
Banking
8.6%
Information Technology and Services
8.6%
Education Management
5.7%
Financial Services
5.7%
Other
58.6%
Alternatives
Discussions
What data visualization and reporting methods do you support?
2 Comments
Official Response from Dataiku
Dataiku supports a number of tools for engaging stakeholders and team members, including:
- 25 types of interactive charts
- Dashboards that allow you...Read more
Can I securely work on my sensitive data?
2 Comments
PB
Hi,
DSS is on-premise software but we also provide a Amazon image and Docker if desired. You can try DSS on your own computer for free by downloading the...Read more
What machine learning algorithms do you support?
1 Comment
Official Response from Dataiku
Dataiku leverages machine learning libraries from scikit-learn, Spark MLlib, H2O and Vertica for guided ML analyses that give you instant visual &...Read more
IBM Cloud Pak for Data has no discussions with answers
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