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- Azure OpenAI Service-vs-IBM watsonx.ai
Compare Azure OpenAI Service and IBM watsonx.ai 
At a Glance
Market Segments
Enterprise (50.0% of reviews)
Small-Business (37.8% of reviews)
Entry-Level Pricing
No pricing available
No pricing available
Azure OpenAI Service
Star Rating
(19)4.7 out of 5
Market Segments
Enterprise (50.0% of reviews)
Entry-Level Pricing
No pricing available
Learn more about Azure OpenAI ServiceIBM watsonx.ai
Star Rating
(79)4.5 out of 5
Market Segments
Small-Business (37.8% of reviews)
Entry-Level Pricing
No pricing available
Free Trial is available
Learn more about IBM watsonx.aiSummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- Users report that IBM watsonx.ai excels in data quality with a score of 9.7, which reviewers mention significantly enhances the reliability of their AI models, while Azure OpenAI Service, although strong, has a slightly lower score of 9.2 in this area.
- Reviewers mention that Azure OpenAI Service shines in scalability with a score of 9.5, making it a preferred choice for enterprises looking to expand their AI capabilities, whereas IBM watsonx.ai has a solid score of 9.0 but lacks the same level of enterprise scalability.
- Users on G2 highlight that IBM watsonx.ai offers superior data ingestion and wrangling capabilities with a score of 8.2, which reviewers say simplifies the process of preparing data for analysis, compared to Azure OpenAI Service's more basic offerings in this area.
- G2 users report that Azure OpenAI Service provides better contextual understanding with a score of 8.4, which reviewers mention is crucial for applications requiring nuanced interactions, while IBM watsonx.ai scored slightly lower at 8.4.
- Reviewers mention that IBM watsonx.ai's model registry feature is highly rated at 9.3, allowing for effective management of AI models, whereas Azure OpenAI Service's model management features are rated at 8.3, indicating room for improvement.
- Users say that Azure OpenAI Service's natural language processing capabilities are rated at 8.8, which reviewers highlight as a strong point for applications focused on text analysis, while IBM watsonx.ai's score of 8.0 in this area suggests it may not be as robust for similar tasks.
Pricing
Entry-Level Pricing
Azure OpenAI Service
No pricing availableIBM watsonx.ai
No pricing availableFree Trial
Azure OpenAI Service
No trial information availableIBM watsonx.ai
Free Trial is available
Ratings
Meets Requirements
9.0
16
8.9
63
Ease of Use
8.8
16
9.1
72
Ease of Setup
9.0
13
9.1
64
Ease of Admin
8.3
8
8.6
32
Quality of Support
8.5
16
8.8
62
Has the product been a good partner in doing business?
9.3
7
8.9
32
Product Direction (% positive)
10.0
15
9.8
63
Features by Category
Deployment
Not enough data
9.2
8
Not enough data
8.3
8
Not enough data
8.1
8
Not enough data
9.2
8
Not enough data
9.2
8
Deployment
Not enough data
9.2
8
Not enough data
8.5
8
Not enough data
8.3
8
Not enough data
9.2
8
Not enough data
9.6
8
Management
Not enough data
8.3
8
Not enough data
9.0
8
Not enough data
8.5
8
Not enough data
9.4
8
Operations
Not enough data
9.0
8
Not enough data
8.5
8
Not enough data
9.4
8
Management
Not enough data
8.5
8
Not enough data
9.0
7
Not enough data
8.6
7
Generative AI
Not enough data
9.2
8
Not enough data
9.4
8
System
Not enough data
8.3
28
Not enough data
8.8
29
Not enough data
8.2
29
Model Development
Not enough data
8.6
29
Not enough data
8.2
29
Not enough data
8.7
28
Not enough data
8.3
29
Model Development
Not enough data
8.6
29
Not enough data
8.6
29
Not enough data
8.4
29
Machine/Deep Learning Services
Not enough data
Feature Not Available
Not enough data
8.9
29
Not enough data
8.6
29
Not enough data
8.1
29
Machine/Deep Learning Services
Not enough data
Feature Not Available
Not enough data
8.4
29
Not enough data
8.7
29
Not enough data
8.7
29
Deployment
Not enough data
8.2
29
Not enough data
8.6
29
Not enough data
8.6
29
Deployment
Not enough data
8.5
28
Not enough data
8.8
28
Not enough data
8.6
28
Generative AI
Not enough data
8.8
28
Not enough data
8.8
28
Not enough data
Feature Not Available
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
Not enough data
9.2
11
Not enough data
Feature Not Available
Not enough data
8.8
10
Synthesis Type
Not enough data
9.2
10
Not enough data
9.2
10
Data Transformation
Not enough data
8.8
10
Not enough data
9.7
10
Not enough data
9.7
10
Not enough data
9.2
10
Not enough data
9.2
10
Scalability and Performance - Generative AI Infrastructure
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Cost and Efficiency - Generative AI Infrastructure
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Integration and Extensibility - Generative AI Infrastructure
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Security and Compliance - Generative AI Infrastructure
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Usability and Support - Generative AI Infrastructure
Not enough data
Not enough data
Not enough data
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
Not enough data
8.9
18
Learning - Machine Learning
Not enough data
9.1
19
Not enough data
9.1
19
Not enough data
9.1
18
Large Language Model Operationalization (LLMOps)Hide 15 FeaturesShow 15 Features
Not enough data
Not enough data
Prompt Engineering - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Not enough data
Not enough data
Inference Optimization - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Model Garden - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Custom Training - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Application Development - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Model Deployment - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Not enough data
Not enough data
Guardrails - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Not enough data
Not enough data
Model Monitoring - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Not enough data
Not enough data
Security - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Not enough data
Not enough data
Gateways & Routers - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Customization - AI Agent Builders
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Functionality - AI Agent Builders
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 and Analytics - AI Agent Builders
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Integration - AI Agent Builders
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
Azure OpenAI Service and IBM watsonx.ai are categorized as Machine Learning
Unique Categories
Azure OpenAI Service has no unique categories
IBM watsonx.ai is categorized as Synthetic Data, MLOps Platforms, Data Science and Machine Learning Platforms, AI Content Creation Platforms, Generative AI Infrastructure, Large Language Model Operationalization (LLMOps), and AI Agent Builders
Reviews
Reviewers' Company Size
Azure OpenAI Service
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
IBM watsonx.ai
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
38.9%
Computer Software
22.2%
Public Relations and Communications
5.6%
Oil & Energy
5.6%
Mental Health Care
5.6%
Other
22.2%
Information Technology and Services
21.6%
Computer Software
12.2%
Marketing and Advertising
8.1%
Market Research
8.1%
Consulting
5.4%
Other
44.6%
Most Helpful Reviews
Azure OpenAI Service
Most Helpful Favorable Review
Verified User in Computer Software
Azure Open AI Service has a wide range of tools like natural process language, and recognizing the image these are very useful for business as they can handle lots of work which reduces human efforts.
Most Helpful Critical Review
IBM watsonx.ai
Most Helpful Favorable Review
Pradeep P.
Verified User in Airlines/Aviation
A very powerful tool used to enable corporate businesses to build and deploy conversational AI SOLUTIONS . Strong mutli channel support including websites, mobile app and messaging platform. Built in machine learning capabilities Seamless integration...
Most Helpful Critical Review
Verified User in Consulting
Regarding integration, running wx.ai in a python notebook is still very slow when compared to having a locally hosted LLM. Building more complex models and adding up governance to the equation makes the interface less friendly.
Verified User in Computer Software
Azure Open AI Service has a wide range of tools like natural process language, and recognizing the image these are very useful for business as they can handle lots of work which reduces human efforts.
Pradeep P.
Verified User in Airlines/Aviation
A very powerful tool used to enable corporate businesses to build and deploy conversational AI SOLUTIONS . Strong mutli channel support including websites, mobile app and messaging platform. Built in machine learning capabilities Seamless integration...
Verified User in Consulting
Regarding integration, running wx.ai in a python notebook is still very slow when compared to having a locally hosted LLM. Building more complex models and adding up governance to the equation makes the interface less friendly.
Alternatives
Discussions
Azure OpenAI Service has no discussions with answers
IBM watsonx.ai has no discussions with answers
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