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 #13 with an average rating of 8.0. Microsoft holds a 12.0% mindshare in AIDP, compared to IBM’s 1.8% mindshare. Additionally, 93% of Microsoft users are willing to recommend the solution, compared to 100% of IBM users who would recommend it.

Azure OpenAI

Read 33 Azure OpenAI reviews

8,663 Views
7,905 Comparison Views

93% willing to recommend

IBM Watson Machine Learning

Read 7 IBM Watson Machine Learning reviews

1,226 Views
950 Comparison Views

100% willing to recommend

Azure OpenAI

IBM Watson Machine Learning

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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.

To learn more, read our detailed Azure OpenAI vs. IBM Watson Machine Learning Report (Updated: April 2025).

Buyer's Guide

Azure OpenAI vs. IBM Watson Machine Learning

April 2025

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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:

Categories and Ranking

Azure OpenAI

Ranking in AI Development Platforms

1st

Average Rating

7.8

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

No ranking in other categories

IBM Watson Machine Learning

Ranking in AI Development Platforms

13th

Average Rating

8.0

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of May 2025, in the AI Development Platforms category, the mindshare of Azure OpenAI is 12.0%, down from 20.8% compared to the previous year. The mindshare of IBM Watson Machine Learning is 1.8%, down from 2.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.

AI Development Platforms

Featured Reviews

Viswanath Barenkala

Associate Vice President at State Street

Offers tools to moderate generated content and guidance to safely design applications, but it is not consistently accessible

Instead of a feature, the GPT-4 model has been most beneficial for automating tasks. We transitioned from GPT-3.5 to GPT-4 and actively use it. However, we face limitations due to geographic availability, subscription constraints, and rate limiting, which we are currently negotiating and working towards optimizing. While we haven't formally benchmarked Azure OpenAI's language understanding against industry standards, we find it performs well about 70-80% of the time. Occasionally, we need to refine our queries and adapt our systems accordingly to improve accuracy and effectiveness.

Read full review

Anurag Mayank

Manager at Maruti Suzuki India Limited

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.

Read full review

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"The product's initial setup phase was pretty easy."

"Its versatility makes it incredibly useful for technical problem-solving, content creation, data analytics, and more."

"Azure OpenAI is useful for benchmarking products."

"The most crucial aspect is the conversational capability, where you can simply ask questions, and it provides answers based on your content and documents, particularly tailored to your specific environment."

"It's very powerful. It allows users to query our documents using natural language and receive answers in the same way. This makes our product information much more accessible than traditional keyword-based search."

"We have many use cases for the solution, such as digitalizing records, a chatbot looking at records, and being able to use generative AI on them."

"OpenAI's models are more mature than Watson's. They offer a wider range of features and provide richer outputs."

"We can use the solution to implement our tasks and models quickly."

More Azure OpenAI pros

"The solution is very valuable to our organization due to the fact that we can work on it as a workflow."

"Scalability-wise, I rate the solution ten out of ten."

"It is has a lot of good features and we find the image classification very useful."

"It has improved self-service and customer satisfaction."

"The most valuable aspect of the solution's the cost and human labor savings."

"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."

"We can enable and change developer productivity with artificial intelligence-recommended code based on natural language input or exciting source code."

Cons

"There could be an ability to generate visual data, such as architecture diagrams."

"Since we don't train the model on our data, it's a struggle to ensure OpenAI answers questions exclusively from our data. During user testing, we found ways to make the system provide answers from outside sources."

"Deployment was slightly complex for me to understand."

"The solution needs to accommodate smaller companies."

"The fine-tuning of models with the use of Azure OpenAI is an area with certain shortcomings currently, and it can be considered for improvement in the future."

"There are no available updates of information that are currently provided."

"One area for improvement is providing more flexibility in configuration and connectivity with external tools."

"I faced one issue with Azure OpenAI: My customer wanted more clarity on the pricing. They were not able to get proper answers from the documentation or the pricing calculator. I suggest that Microsoft maintain standardization in the pricing details published in the documentation and the pricing calculator."

More Azure OpenAI cons

"The supporting language is limited."

"Sometimes training the model is difficult."

"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."

"Scaling is limited in some use cases. They need to make it easier to expand in all aspects."

"They should add more GPU processing power to improve performance, especially when dealing with large amounts of data."

"Honestly, I haven't seen any comparative report that has run the same data through two different artificial intelligence or machine learning capabilities to get something out of it. I would love to see that."

"In future releases, I would like to see a more flexible environment."

Pricing and Cost Advice

"The cost is quite high and fixed."

"According to the negotiations taking place and the contract, there is a need to make either monthly or yearly payments to use the solution."

"While the product meets our business requirements well, I consider it relatively expensive, especially for individual users like myself."

"It's a token-based system, so you pay per token used by the model."

"The tool costs around 20 dollars a month."

"Azure OpenAI is a bit more expensive than other services."

"The platform offers a flexible pricing model which depends on the features and capabilities we utilize."

"The cost structure depends on the volume of data processed and the computational resources required."

More Azure OpenAI pricing and cost advice

"I've only been using the free tier, but it's quite competitive on a service basis."

"The pricing model is good."

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Top Industries

By visitors reading reviews

Financial Services Firm

15%

Computer Software Company

13%

Manufacturing Company

11%

Educational Organization

Computer Software Company

16%

Financial Services Firm

11%

Educational Organization

11%

University

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?

In the past, the primary expense involved token limitations which constrained scaling. Recent iterations have increased token allowances, mitigating some challenges associated with concurrent user ...

See all answers

What needs improvement with Azure OpenAI?

Azure ( /products/microsoft-azure-reviews ) could significantly benefit from including more LLM models apart from OpenAI, as I often need to switch clouds when a model doesn't meet my requirements....

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 42% of the time

Hugging Face vs Azure OpenAI

Compared 14% of the time

Amazon SageMaker vs Azure OpenAI

Compared 12% of the time

Microsoft Azure Machine Learning Studio vs Azure OpenAI

Compared 8% of the time

Together Inference vs Azure OpenAI

Compared 2% of the time

More Azure OpenAI Competitors

Google Cloud AI Platform vs IBM Watson Machine Learning

Compared 65% of the time

TensorFlow vs IBM Watson Machine Learning

Compared 10% of the time

Google Vertex AI vs IBM Watson Machine Learning

Compared 8% of the time

IBM Watson Studio vs IBM Watson Machine Learning

Compared 5% of the time

More IBM Watson Machine Learning Competitors

Product Reports

Buyer's Guide

Azure OpenAI

April 2025

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Buyer's Guide

AI Development Platforms

March 2025

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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.

Microsoft

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.

IBM

Buyer's Guide

Azure OpenAI vs. IBM Watson Machine Learning

April 2025

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