IBM Watson Machine Learning vs TensorFlow comparison

IBM Watson Machine Learning

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Executive SummaryUpdated on Dec 4, 2024

TensorFlow and IBM Watson Machine Learning compete in the machine learning space. TensorFlow should be preferred for users seeking cost-effective solutions due to its comprehensive open-source features, while IBM Watson Machine Learning is superior for enterprises demanding extensive features and willing to invest more.

Features: TensorFlow offers flexibility in model building, a wide array of libraries for deep learning, and scalability for high-performance computations. IBM Watson Machine Learning provides automated model building, excellent natural language processing capabilities, and seamless integration with IBM Cloud.

Room for Improvement: TensorFlow could enhance its user interface for beginners, offer more comprehensive support for non-Python environments, and improve visualization tools. IBM Watson Machine Learning can refine its pricing structure, expand support for non-IBM cloud environments, and reduce complexity in setup and customization.

Ease of Deployment and Customer Service: TensorFlow is straightforward to deploy, particularly in Python-friendly environments, and benefits from a robust community for support. IBM Watson Machine Learning ensures a smooth deployment via IBM Cloud and offers dedicated customer support, providing a reliable environment for large enterprises.

Pricing and ROI: TensorFlow's open-source nature leads to minimal setup costs, appealing to budget-conscious developers seeking high ROI through custom implementations. IBM Watson Machine Learning entails higher initial costs but includes premium features and support, presenting significant ROI for enterprises ready to invest in comprehensive solutions.

To learn more, read our detailed IBM Watson Machine Learning vs. TensorFlow Report (Updated: June 2025).

IBM Watson Machine Learning vs. TensorFlow

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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

IBM Watson Machine Learning

Ranking in AI Development Platforms

14th

Average Rating

8.0

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

No ranking in other categories

TensorFlow

Ranking in AI Development Platforms

6th

Average Rating

8.8

Reviews Sentiment

7.4

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of July 2025, in the AI Development Platforms category, the mindshare of IBM Watson Machine Learning is 1.8%, down from 2.2% compared to the previous year. The mindshare of TensorFlow is 4.4%, down from 6.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.

AI Development Platforms

Featured Reviews

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.

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Dan Bryant

CEO at II4Tech

A strong solution for providing insight into machine learning strategies

I'm not a professional with machine learning. Early on, I was working with data scientists and built a platform for some old-school data scientists to turn around their models faster, and they were focused on electric prices. Based on that experience and my understanding of our value, I'm researching all the machine learning tools. I realized I would have to be a specialist in any of them, and my main skillset is in systems engineering and data engines. I look forward to being an analytics specialist. In real life, I would be better off hiring a professional because when I decide which tool I want to use for what job, I could hire that professional. They would be valuable to me across the whole of what we do. It's kinda of what I do when I build hardware and new products or do version upgrades. I hire a team just for production that are experts in their particular field, so I get production-quality pieces. At that point, my internal team can add the necessary analytics or automation. Hopefully, anyone getting the solution already knows what they will use it for. If they're starting from scratch, I strongly recommend hiring a consultant. I rate TensorFlow an eight out of ten because, for my intents and purposes, I don't know what else one can use to get into the machine learning game if you're going to export models.

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Quotes from Members

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

Pros

"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."

"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."

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

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

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

"TensorFlow is an efficient product for building neural networks."

"Our clients were not aware they were using TensorFlow, so that aspect was transparent. I think we personally chose TensorFlow because it provided us with more of the end-to-end package that you can use for all the steps regarding billing and our models. So basically data processing, training the model, evaluating the model, updating the model, deploying the model and all of these steps without having to change to a new environment."

"The available documentation is extensive and helpful."

"Optimization is very good in TensorFlow. There are many opportunities to do hyper-parameter training."

"It provides us with 35 features like patch normalization layers, and it is easy to implement using the Kras library when the Kaspersky flow is running behind it."

"The most valuable feature of TensorFlow is deep learning. It is the best tool for deep learning in the market."

"It is easy to use and learn."

"TensorFlow is a framework that makes it really easy to use for deep learning."

More TensorFlow pros

Cons

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

"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."

"Sometimes training the model is difficult."

"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."

"The supporting language is limited."

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

"It would be cool if TensorFlow could make it easier for companies like us to program for running it across different hyperscalers."

"Enhancements could include increasing use cases and improving the accuracy of previously built models in TensorFlow. For instance, when we run certain models, the computing power of laptops becomes high."

"The process of creating models could be more user-friendly."

"JavaScript is a different thing and all the websites and web apps and all the mobile apps are built-in JavaScript. JavaScript is the core of that. However, TensorFlow is like a machine learning item. What can be improved with TensorFlow is how it can mix in how the JavaScript developers can use TensorFlow."

"The solution is hard to integrate with the GPUs."

"TensorFlow deep learning takes a lot of computation power. The more systems you can use, the easier it is. That's a good ability, if you can make a system run immediately at the same time on the same task, it's much faster rather than you having one system running which is slower. Running systems in parallel is a complex situation, but it can improve. There is a lot of work involved."

"It would be nice if the solution was in Hungarian. I would like more Hungarian NAT models."

"It doesn't allow for fast the proto-typing. So usually when we do proto-typing we will start with PyTorch and then once we have a good model that we trust, we convert it into TensorFlow. So definitely, TensorFlow is not very flexible."

More TensorFlow cons

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."

"The solution is free."

"TensorFlow is free."

"It is open-source software. You don't have to pay all the big bucks like Azure and Databricks."

"I did not require a license for this solution. It a free open-source solution."

"I am using the open-source version of TensorFlow and it is free."

"We are using the free version."

"I think for learners to deploy a project, you can actually use TensorFlow for free. It's just amazing to have an open-source platform like TensorFlow to deploy your own project. Here in Russia no one really cares about licenses, as it is totally open source and free. My clients in the United States were also pleased to learn when they enquired, that licensing is free."

"I rate TensorFlow's pricing a five out of ten."

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Top Industries

By visitors reading reviews

Computer Software Company

15%

Financial Services Firm

11%

University

10%

Educational Organization

Manufacturing Company

15%

Computer Software Company

12%

Financial Services Firm

University

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

Questions from the Community

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.

What is your experience regarding pricing and costs for IBM Watson Machine Learning?

The solution is expensive.

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...

What do you like most about TensorFlow?

It empowers us to seamlessly create and deploy machine learning models, offering a versatile solution for implementing sophisticated environments and various types of AI solutions.

What is your experience regarding pricing and costs for TensorFlow?

I am not familiar with the pricing setup cost and licensing.

What needs improvement with TensorFlow?

Providing more control by allowing users to build custom functions would make TensorFlow a better option. It currently offers inbuilt functions, however, having the ability to implement custom libr...

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Compared 31% of the time

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Product Reports

AI Development Platforms

Download IBM Watson Machine Learning product report

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Overview

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

TensorFlow is an open source software library for high performance numerical computation. Its flexible architecture allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices. Originally developed by researchers and engineers from the Google Brain team within Google’s AI organization, it comes with strong support for machine learning and deep learning and the flexible numerical computation core is used across many other scientific domains.

Sample Customers

Information Not Available

Airbnb, NVIDIA, Twitter, Google, Dropbox, Intel, SAP, eBay, Uber, Coca-Cola, Qualcomm

IBM Watson Machine Learning vs. TensorFlow