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Azure OpenAI vs IBM Watson Machine Learning comparison

Microsoft and IBM are both solutions in the AI Development Platforms category. Microsoft is ranked #1 with an average rating of 7.9, while IBM is ranked #11 with an average rating of 8.5. Microsoft holds a 22.2% mindshare in AIDP, compared to IBM’s 2.7% mindshare. Additionally, 92% of Microsoft users are willing to recommend the solution, compared to 100% of IBM users who would recommend it.
Azure OpenAI
Read 30 Azure OpenAI reviews
  • 8,392 Views
  • 7,803 Comparison Views
92% willing to recommend
IBM Watson Machine Learning
Read 7 IBM Watson Machine Learning reviews
  • 1,331 Views
  • 1,017 Comparison Views
100% willing to recommend
 

Comparison Buyer's Guide

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Executive SummaryUpdated on Dec 4, 2024

IBM Watson Machine Learning and Azure OpenAI compete in the AI and machine learning platform category. Azure OpenAI holds the upper hand with its advanced functionalities and seamless integration, despite IBM Watson Machine Learning's competitive pricing and customer satisfaction.

Features: IBM Watson Machine Learning provides automation capabilities, integration with IBM services, and a streamlined model deployment. It excels in assisting conversational AI applications and image classification. Azure OpenAI offers robust natural language processing, seamless Microsoft ecosystem integration, and comprehensive data generation capabilities. It is known for effective document summarization and real-time querying through ChatGPT models.

Room for Improvement: IBM Watson Machine Learning could benefit from expanding its AI model training options, enhancing natural language processing features, and optimizing deployment scalability. Azure OpenAI may improve by addressing geographic availability, subscription constraints, and rate limits. Efforts to reduce initial setup complexity and offer more cost-effective solutions would also be beneficial.

Ease of Deployment and Customer Service: IBM Watson Machine Learning offers straightforward implementation processes with dedicated support that eases AI transformation journeys. Azure OpenAI provides flexible deployment options backed by Microsoft's extensive documentation and support. Its adaptability in deployment is balanced by IBM's valuable customer service.

Pricing and ROI: IBM Watson Machine Learning features a competitive pricing model with clear ROI through decision-making enhancements. Azure OpenAI incurs higher initial costs yet promises higher ROI with advanced automation and AI capabilities, attracting organizations focused on cutting-edge AI.

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Buyer's Guide
Azure OpenAI vs. IBM Watson Machine Learning
December 2024
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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:
 

Customer Service

Sentiment score
5.8
Azure OpenAI's support is professional yet inconsistent, with subscription users receiving better assistance than free users.
No sentiment score available
 

Room For Improvement

Sentiment score
4.4
Azure OpenAI must improve model fine-tuning, language support, integration, stability, scalability, and user services amidst technological updates.
No sentiment score available
 

Scalability Issues

Sentiment score
6.6
Azure OpenAI is scalable but faces challenges with high loads, costs, and regional capacity, affecting ROI in smaller deployments.
No sentiment score available
 

Stability Issues

Sentiment score
7.7
Azure OpenAI is praised for stability despite occasional performance issues, generally offering a reliable experience during high usage.
Sentiment score
8.1
IBM Watson Machine Learning is highly stable and reliable, with users reporting no downtime and minimal maintenance issues.
 

Valuable Features

Sentiment score
8.0
Azure OpenAI excels in automation, accuracy, and adaptability, integrating smoothly with tools to enhance productivity and support versatile applications.
Sentiment score
7.9
IBM Watson Machine Learning streamlines processes with automation, aiding integration, predictive analytics, decision-making, and enhancing generative AI capabilities.
 

Setup Cost

No sentiment score available
Sentiment score
5.6
IBM Watson Machine Learning pricing receives mixed reviews; it's competitive for some, costly for others, with usage-based costs.
 

Categories and Ranking

Azure OpenAI
Ranking in AI Development Platforms
1st
Average Rating
8.0
Reviews Sentiment
6.7
Number of Reviews
30
Ranking in other categories
No ranking in other categories
IBM Watson Machine Learning
Ranking in AI Development Platforms
11th
Average Rating
8.0
Reviews Sentiment
7.1
Number of Reviews
7
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of December 2024, in the AI Development Platforms category, the mindshare of Azure OpenAI is 22.2%, up from 18.4% compared to the previous year. The mindshare of IBM Watson Machine Learning is 2.7%, up from 2.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.
AI Development Platforms
 

Featured Reviews

Anup Karade - PeerSpot reviewer
Created a chatbot powered by OpenAI to answer HR, travel, and expense-related questions
We have created our own platform called CanvasAI. It provides machine-learning plumbing and integration with your services. So, we've integrated Azure OpenAI through Canvas. We're also looking at some hard interface modeling from AWS as well. We access Azure OpenAI solutions for our business workflow via the platform, not directly from the service provider. There are security considerations we're working through with the security team, etc. Canvas provides the control plane, which handles RBAC for user registration and model management. For internal processes, we're using OpenAI to create partner call reports, like summarizing quarterly zip code data and other financial models. We also use it for legal and contract stuff, like comparing SOWs and contracts. For user experience, we've created a chatbot powered by OpenAI to answer HR, travel, and expense-related questions.
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Anurag Mayank - PeerSpot reviewer
A highly efficient solution that delivers the desired results to its users
I had not considered how the solution could be improved because I was focused on how it was helping me to solve my issues. If I consider how we want to use it in our organization, certain areas of improvement can be addressed. For instance, we want to use it with Generative AI, not like ChatGPT, but in a way intended for industrial use. It would be beneficial to incorporate more AI into the solution.
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Top Industries

By visitors reading reviews
Financial Services Firm
15%
Computer Software Company
13%
Manufacturing Company
11%
Educational Organization
6%
Computer Software Company
14%
University
13%
Educational Organization
12%
Financial Services Firm
11%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
 

Questions from the Community

What do you like most about Azure OpenAI?
The product is easy to integrate with our IT workflow.
See all answers
What is your experience regarding pricing and costs for Azure OpenAI?
The pricing really depends on the specific requirements and underlying needs. For example, if the goal is to implement innovative solutions for the future or to improve productivity in decision-mak...
See all answers
What needs improvement with Azure OpenAI?
For my needs, when working with interactive dashboards, it's expensive. I would prefer a system that provides alternative dashboard options or allows me to go directly into the program and pinpoint...
See all answers
What do you like most about IBM Watson Machine Learning?
I was particularly interested in trying the AutoML feature to see how it handles data and proposes new models. The variety of models it provides is impressive.
See all answers
What is your experience regarding pricing and costs for IBM Watson Machine Learning?
The solution is expensive.
See all answers
What needs improvement with IBM Watson Machine Learning?
Sometimes training the model is difficult. We need to have at least a few different components to evaluate and understand the behavior of different users to have a very, very high accuracy in the m...
See all answers
 

Comparisons

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Compared 9% of the time
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Compared 2% of the time
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Compared 68% of the time
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TensorFlow vs IBM Watson Machine Learning
Compared 10% of the time
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Compared 4% of the time
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Product Reports

Buyer's Guide
Azure OpenAI
December 2024
Azure OpenAI reportDownload Azure OpenAI product report
Buyer's Guide
AI Development Platforms
December 2024
IBM Watson Machine Learning reportDownload IBM Watson Machine Learning product report
 

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Microsoft
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IBM
 

Overview

Azure OpenAI integrates advanced language models with robust security for precise information extraction and task automation. Its seamless Azure integration and drag-and-drop interface simplify implementation and enhance accessibility.

Azure OpenAI offers a comprehensive suite of features designed for efficient data processing and task automation. It provides high precision in extracting information and strong conversational capabilities, crucial for developing chatbots and customer support systems. Its integration with Azure ensures seamless data handling and security, addressing key enterprise requirements. Users can employ its versatile GPT models for diverse applications such as predictive analytics, summarizing large documents, and competitive benchmarking. Despite its strengths, it faces challenges like latency, inadequate regional support, and limited integration of new technologies. Improvements in model fine-tuning and more flexible configuration are desired by users.

What features make Azure OpenAI a reliable choice?
  • High Precision: Ensures accurate data extraction suitable for critical applications.
  • Conversational Capabilities: Facilitates the creation of sophisticated chatbots.
  • Azure Integration: Seamlessly connects with Azure services for enhanced functionality.
  • Automation Support: Automates repetitive tasks, improving efficiency.
  • Security Features: Offers robust protection for sensitive data.
  • Drag-and-Drop Environment: Simplifies user interaction and deployment.
What benefits and ROI should users consider?
  • Natural Language Processing: Provides superior language model performance.
  • Quick Implementation: Reduces time to deploy applications.
  • Scalability: Supports growing business needs with ease.
  • Versatile GPT Models: Applicable across different industry workflows.

Azure OpenAI is implemented across industries like healthcare, finance, and education for tasks like invoice processing, digitalizing records, and language translation. It enhances policy management, document assimilation, and customer support with predictive analytics and keyword extraction. Organizations in such sectors benefit from streamlined workflows and task automation.

IBM Watson Machine Learning helps data scientists and developers accelerate AI and machine-learning deployment. With its open, extensible model operation, Watson Machine Learning helps businesses simplify and harness AI at scale across any cloud.

Buyer's Guide
Azure OpenAI vs. IBM Watson Machine Learning
December 2024
Free Report: Azure OpenAI vs. IBM Watson Machine Learning
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