IBM Watson Machine Learning vs TensorFlow comparison

IBM Watson Machine Learning

Read 20 TensorFlow reviews

4,063 Views
2,363 Comparison Views

100% willing to recommend

Comparison Buyer's Guide

Download the report

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: April 2025).

IBM Watson Machine Learning vs. TensorFlow

April 2025

Download the complete report

Helped 851,604 peers since 2012

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 May 2025, in the AI Development Platforms category, the mindshare of IBM Watson Machine Learning is 1.8%, down from 2.3% compared to the previous year. The mindshare of TensorFlow is 3.8%, down from 6.7% 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.

Read full review

Ashish Upadhyay

Founder at BlockMosiac

A robust tools for model visualization and debugging with superior scalability and stability, and an intuitive user-friendly interface

The one feature we find most valuable at our company is its robust and flexible machine-learning capabilities. 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. The ability to develop and fine-tune models, such as risk assessment for detection and market protection, as well as the creation of recommendation systems, is paramount. This versatility extends to providing personalized identity-relevant applications for our enterprise clients, delivering valuable insights to the market. Its exceptional support for deep learning and its efficient resource utilization enable us to undertake complex financial and data analyses. The flexibility it provides is crucial for meeting industrial requirements and crafting solutions tailored to our client's specific needs.

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

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

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

"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."

"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 most valuable features are the frameworks and the functionality to work with different data, even when we have a certain quantity of data flowing."

"It's got quite a big community, which is useful."

"Google is behind TensorFlow, and they provide excellent documentation. It's very thorough and very helpful."

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

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

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

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

"TensorFlow improves my organization because our clients get a lot of investment from their investors and we are progressively improving the products. Every six months we release new features."

More TensorFlow pros

Cons

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

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

"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."

"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."

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

"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."

"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."

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

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

"For newcomers to the field, the learning curve can be steep, often requiring about a year of dedicated effort."

"There are connection issues that interrupt the download needed for the data sets. We need to prepare them ourselves."

"However, if I want to change just one thing in the implementation of TensorFlow functions I have to copy everything that they wrote and I change it manually if indeed it can be amended. This is really hard as it's written in C++ and has a lot of complications."

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

More TensorFlow cons

Pricing and Cost Advice

"The pricing model is good."

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

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

"It is an open-source solution, so anyone can use it free of charge."

"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."

"We are using the free version."

"The solution is free."

"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."

"TensorFlow is free."

More TensorFlow pricing and cost advice

See which vendors are best for you

Use our free recommendation engine to learn which AI Development Platforms solutions are best for your needs.

See recommendations

851,604 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Computer Software Company

16%

Financial Services Firm

11%

University

11%

Educational Organization

10%

Manufacturing Company

14%

Computer Software Company

13%

University

Financial Services Firm

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...

Google Cloud AI Platform vs IBM Watson Machine Learning

Comparisons

Compared 66% of the time

Azure OpenAI vs IBM Watson Machine Learning

Compared 12% 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

OpenVINO vs TensorFlow

Compared 24% of the time

Microsoft Azure Machine Learning Studio vs TensorFlow

Compared 17% of the time

Google Vertex AI vs TensorFlow

Compared 16% of the time

PyTorch vs TensorFlow

Compared 13% of the time

MXNet vs TensorFlow

Compared 2% of the time

More TensorFlow Competitors

Product Reports

Download IBM Watson Machine Learning product report

AI Development Platforms

May 2025

Download TensorFlow product report

April 2025

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