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At a Glance
Market Segments
Small-Business (40.5% of reviews)
Enterprise (49.3% of reviews)
Entry-Level Pricing
No pricing available
No pricing available
IBM watsonx.ai
Star Rating
(122)4.4 out of 5
Market Segments
Small-Business (40.5% of reviews)
Entry-Level Pricing
No pricing available
Free Trial is available
Learn more about IBM watsonx.aiSAS Visual Data Mining and Machine Learning
Star Rating
(85)4.6 out of 5
Market Segments
Enterprise (49.3% of reviews)
Entry-Level Pricing
No pricing available
Learn more about SAS Visual Data Mining and Machine LearningSummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- Users report that SAS Visual Data Mining and Machine Learning excels in data ingestion and wrangling with a score of 8.8, while IBM watsonx.ai received a lower score of 8.2 in the same area, indicating that SAS may offer a more robust solution for preparing data for analysis.
- Reviewers mention that IBM watsonx.ai shines in natural language processing with a score of 8.8, compared to SAS's score of 7.5, suggesting that IBM's capabilities in understanding and generating human language are superior, making it a better choice for applications requiring advanced NLP.
- G2 users highlight that SAS Visual Data Mining and Machine Learning provides a more user-friendly drag and drop interface with a score of 9.0, while IBM watsonx.ai scored 8.1, indicating that SAS may be easier for users to navigate and utilize effectively.
- Users on G2 report that IBM watsonx.ai has a higher score of 8.9 for natural language generation, compared to SAS's score of 7.0, suggesting that IBM's features for generating text are more advanced and may lead to better user experiences in applications requiring text generation.
- Reviewers say that SAS Visual Data Mining and Machine Learning has a strong focus on model training with a score of 9.0, while IBM watsonx.ai scored 8.2, indicating that SAS may provide more effective tools for training machine learning models.
- Users report that IBM watsonx.ai excels in data privacy protection with a score of 8.6, while SAS scored 8.2, suggesting that IBM may offer more robust features for ensuring compliance and safeguarding sensitive data.
Pricing
Entry-Level Pricing
IBM watsonx.ai
No pricing availableSAS Visual Data Mining and Machine Learning
No pricing availableFree Trial
IBM watsonx.ai
Free Trial is available
SAS Visual Data Mining and Machine Learning
No trial information availableRatings
Meets Requirements
8.8
77
8.5
24
Ease of Use
8.9
109
8.4
28
Ease of Setup
8.5
100
8.5
18
Ease of Admin
8.7
36
8.9
14
Quality of Support
8.8
76
9.0
23
Has the product been a good partner in doing business?
8.9
36
8.9
15
Product Direction (% positive)
9.9
79
9.1
25
Features by Category
Deployment
9.1
9
Not enough data
8.5
9
Not enough data
7.8
9
Not enough data
8.7
9
Not enough data
8.7
9
Not enough data
Deployment
9.3
9
Not enough data
8.7
9
Not enough data
8.3
9
Not enough data
8.9
9
Not enough data
9.1
9
Not enough data
Management
8.0
9
Not enough data
8.5
9
Not enough data
8.5
9
Not enough data
9.3
9
Not enough data
Operations
9.1
9
Not enough data
8.7
9
Not enough data
9.3
9
Not enough data
Management
8.5
9
Not enough data
9.0
8
Not enough data
8.5
8
Not enough data
Generative AI
9.1
9
Not enough data
9.3
9
Not enough data
System
8.2
31
8.8
8
Model Development
8.6
32
8.1
7
8.2
32
9.0
7
8.7
31
8.6
7
8.4
32
9.0
7
Model Development
8.5
32
7.5
6
Machine/Deep Learning Services
Feature Not Available
8.7
5
8.9
32
7.5
6
8.6
32
7.0
5
8.1
32
8.6
7
Machine/Deep Learning Services
8.5
32
8.7
5
8.8
32
8.6
7
Deployment
8.2
32
8.9
6
8.6
32
8.3
6
8.8
32
8.6
7
Generative AI
8.8
31
Not enough data
8.8
31
Not enough data
Feature Not Available
Not enough data
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 Type
8.8
13
Not enough data
Feature Not Available
Not enough data
8.5
12
Not enough data
Synthesis Type
9.0
12
Not enough data
9.2
12
Not enough data
Data Transformation
8.6
12
Not enough data
9.3
12
Not enough data
9.7
12
Not enough data
9.2
12
Not enough data
9.2
12
Not enough data
Scalability and Performance - Generative AI Infrastructure
9.3
7
Not enough data
8.8
7
Not enough data
9.3
7
Not enough data
Cost and Efficiency - Generative AI Infrastructure
8.3
7
Not enough data
8.6
7
Not enough data
8.3
7
Not enough data
Integration and Extensibility - Generative AI Infrastructure
9.5
7
Not enough data
8.6
7
Not enough data
8.8
7
Not enough data
Security and Compliance - Generative AI Infrastructure
8.3
7
Not enough data
8.8
7
Not enough data
8.6
7
Not enough data
Usability and Support - Generative AI Infrastructure
9.3
7
Not enough data
9.0
7
Not enough data
Content Generation - AI Content Creation Platforms
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Management - AI Content Creation Platforms
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Integration - Machine Learning
9.0
21
Not enough data
Learning - Machine Learning
9.2
22
Not enough data
9.1
22
Not enough data
9.0
21
Not enough data
Prompt Engineering - Large Language Model Operationalization (LLMOps)
9.2
6
Not enough data
8.1
6
Not enough data
Inference Optimization - Large Language Model Operationalization (LLMOps)
8.9
6
Not enough data
Model Garden - Large Language Model Operationalization (LLMOps)
8.9
6
Not enough data
Custom Training - Large Language Model Operationalization (LLMOps)
8.1
6
Not enough data
Application Development - Large Language Model Operationalization (LLMOps)
8.3
6
Not enough data
Model Deployment - Large Language Model Operationalization (LLMOps)
8.3
6
Not enough data
8.6
6
Not enough data
Guardrails - Large Language Model Operationalization (LLMOps)
9.4
6
Not enough data
8.6
6
Not enough data
Model Monitoring - Large Language Model Operationalization (LLMOps)
8.6
6
Not enough data
8.9
6
Not enough data
Security - Large Language Model Operationalization (LLMOps)
9.4
6
Not enough data
9.2
6
Not enough data
Gateways & Routers - Large Language Model Operationalization (LLMOps)
8.9
6
Not enough data
Customization - AI Agent Builders
8.8
7
Not enough data
9.0
7
Not enough data
9.0
7
Not enough data
Functionality - AI Agent Builders
8.6
7
Not enough data
9.0
7
Not enough data
9.3
7
Not enough data
8.8
7
Not enough data
Data and Analytics - AI Agent Builders
9.0
7
Not enough data
8.8
7
Not enough data
9.0
7
Not enough data
Integration - AI Agent Builders
9.0
7
Not enough data
9.0
7
Not enough data
9.0
7
Not enough data
8.6
7
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
Not enough data
8.1
8
Not enough data
8.3
9
Not enough data
8.1
9
Data Analysis
Not enough data
8.5
10
Not enough data
9.0
10
Decision Making
Not enough data
8.6
11
Not enough data
9.1
11
Not enough data
9.1
11
Not enough data
8.5
9
Generative AI
Not enough data
Not enough data
Not enough data
Not enough data
Categories
Categories
Shared Categories
IBM watsonx.ai and SAS Visual Data Mining and Machine Learning are categorized as Data Science and Machine Learning Platforms
Unique Categories
IBM watsonx.ai is categorized as Synthetic Data, MLOps Platforms, Machine Learning, AI Content Creation Platforms, Generative AI Infrastructure, Large Language Model Operationalization (LLMOps), AI Agent Builders, and Low-Code Machine Learning Platforms
SAS Visual Data Mining and Machine Learning is categorized as Predictive Analytics
Reviews
Reviewers' Company Size
IBM watsonx.ai
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
SAS Visual Data Mining and Machine Learning
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
Information Technology and Services
18.9%
Computer Software
11.7%
Consulting
7.2%
Banking
6.3%
Marketing and Advertising
5.4%
Other
50.5%
Computer Software
48.0%
Banking
6.7%
Hospital & Health Care
5.3%
Education Management
5.3%
Financial Services
4.0%
Other
30.7%
Alternatives
Discussions
IBM watsonx.ai has no discussions with answers
SAS Visual Data Mining and Machine Learning has no discussions with answers
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