IBM Watson Machine Learning vs TensorFlow comparison

IBM Watson Machine Learning

Read 19 TensorFlow reviews

3,892 Views
3,892 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: September 2025).

IBM Watson Machine Learning vs. TensorFlow

Download the complete report

Helped 868,706 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

15th

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

7th

Average Rating

8.8

Reviews Sentiment

7.4

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of October 2025, in the AI Development Platforms category, the mindshare of IBM Watson Machine Learning is 2.1%, down from 2.2% compared to the previous year. The mindshare of TensorFlow is 6.6%, up from 4.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.

AI Development Platforms Market Share Distribution
Product	Market Share (%)
TensorFlow	6.6%
IBM Watson Machine Learning	2.1%
Other	91.3%

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

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.

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

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

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

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

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

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

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

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

"TensorFlow provides Insights into both data and machine learning strategies."

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

"TensorFlow is easy to implement and offers inbuilt functions for various tasks."

"The available documentation is extensive and helpful."

"What made TensorFlow so appealing to us is that you could run it on a cluster computer and on a mobile device."

"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."

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

More TensorFlow pros

Cons

"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."

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

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

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

"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."

"The supporting language is limited."

"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."

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

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

"I would love to have a user interface like a programming interface. You need to have a set of menus where you can put things together in a graphical interface. The complete automation of the integration of the modules would also be interesting. It’s more like plumbing as opposed to a fully automated environment."

"It would be nice to have more pre-trained models that we can utilize within layers. I utilize a Mac, and I am unable to utilize AMD GPUs. That's something that I would definitely be like to be able to access within TensorFlow since most of it is with CUDA ML. This only matters for local machines because, in Azure, you can just access any GPU you want from the cloud. It doesn't really matter, but the clients that I work with don't have cloud accounts, or they don't want to utilize that or spend the money. They all see it as too expensive and want to know what they can do on their local machines."

"In terms of improvement, we always look for ways they can optimize the model, accelerate the speed and the accuracy, and how can we optimize with our different techniques. There are various techniques available in TensorFlow. Maintaining accuracy is an area they should work on."

"TensorFlow Lite only outputs to C."

"I know this is out of the scope of TensorFlow, however, every time I've sent a request, I had to renew the model into RAM and they didn't make that prediction or inference. This makes the point for the request that much longer. If they could provide anything to help in this part, it will be very great."

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."

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

"TensorFlow 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."

"We are using the free version."

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

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

"The solution is free."

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

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

868,706 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

University

12%

Computer Software Company

12%

Financial Services Firm

10%

Educational Organization

10%

Manufacturing Company

14%

Computer Software Company

12%

Financial Services Firm

University

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

By reviewers
Company Size	Count
Small Business	12
Midsize Enterprise	3
Large Enterprise	3

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 41% of the time

Azure OpenAI vs IBM Watson Machine Learning

Compared 12% of the time

Google Vertex AI vs IBM Watson Machine Learning

Compared 10% of the time

DataRobot vs IBM Watson Machine Learning

Compared 9% of the time

Dell AI Factory with NVIDIA vs IBM Watson Machine Learning

Compared 8% of the time

More IBM Watson Machine Learning Competitors

PyTorch vs TensorFlow

Compared 34% of the time

OpenVINO vs TensorFlow

Compared 11% of the time

Hugging Face vs TensorFlow

Compared 11% of the time

Google Vertex AI vs TensorFlow

Compared 8% of the time

Google Cloud AI Platform vs TensorFlow

Compared 3% of the time

More TensorFlow Competitors

Product Reports

AI Development Platforms

Download IBM Watson Machine Learning product report

Download TensorFlow product report

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