TensorFlow Reviews

Name: TensorFlow
Brand: TensorFlow
Rating: 4.4 (20 reviews)

4.4 out of 5

20 reviews
100% willing to recommend

What is TensorFlow?

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.

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TensorFlow is the #6 ranked solution in top AI Development Platforms. PeerSpot users give TensorFlow an average rating of 8.8 out of 10. TensorFlow is most commonly compared to Google Vertex AI: TensorFlow vs Google Vertex AI. TensorFlow is popular among the large enterprise segment, accounting for 65% of users researching this solution on PeerSpot. The top industry researching this solution are professionals from a manufacturing company, accounting for 15% of all views.

Helped 856,873 peers since 2012

Featured TensorFlow reviews

Ashish Upadhyay

Founder at BlockMosiac

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.

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Dan Bryant

Owner at II4Tech

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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Jan-Kees Buenen

CEO, co-Founder at SynerScope B.V.

It would be cool if TensorFlow could make it easier for companies like us to program for running it across different hyperscalers. Independent of the hardware platforms or cloud platforms, in this case, we would like to run TensorFlow across different hyperscalers. The hyperscaler platforms are Google, Amazon, and Microsoft, and yes, you can run TensorFlow on all three of them, but you need to make tweaks. From the perspective of an independent software supply and software development company, it would be so much easier if the solution would be pluggable across any infrastructure. We know that that's a tall order because the solution links to the different setups the hyperscalers have. Hardware is not completely and equally set up. If TensorFlow could provide some layer that would make that easy, that would benefit us. Back in 2013-14, though it's now no longer a challenge nor a commercial issue, we were working with the hardware division of IBM. The combination of IBM processors and NVIDIA processors reacted differently to the processor combination of Intel and NVIDIA, which was on the cloud. This meant that we, as an ISV, had to get the solution to work on different hardware.

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TensorFlow mindshare

As of June 2025, the mindshare of TensorFlow in the AI Development Platforms category stands at 3.9%, down from 6.3% compared to the previous year, according to calculations based on PeerSpot user engagement data.

AI Development Platforms

PeerResearch reports based on TensorFlow reviews

Type	Title	Date
Category	AI Development Platforms	Jun 15, 2025	Download
Product	Reviews, tips, and advice from real users	Jun 15, 2025	Download
Comparison	TensorFlow vs Azure OpenAI	Jun 15, 2025	Download
Comparison	TensorFlow vs Google Vertex AI	Jun 15, 2025	Download
Comparison	TensorFlow vs Hugging Face	Jun 15, 2025	Download

Title	Rating	Mindshare	Recommending
Google Vertex AI	4.2	12.5%	100%	10 interviews Add to research
Microsoft Azure Machine Learning Studio	3.9	6.5%	93%	62 interviews Add to research

Valuable Features

"TensorFlow is easy to implement and offers inbuilt functions for various tasks."
"The available documentation is extensive and helpful."
"TensorFlow is an efficient product for building neural networks."

Room for Improvement

"It currently offers inbuilt functions, however, having the ability to implement custom libraries would enhance its usefulness for enterprise-level applications."
"The process of creating models could be more user-friendly."
"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."

Pricing

Enterprise users find TensorFlow's open-source nature appealing, allowing free deployment without licensing costs. They appreciate that the software incurs no setup costs or hidden fees, although additional technical support may require payment. Users across various regions, including Russia and the United States, utilize the complimentary version, favoring it over expensive alternatives like Azure and Databricks. TensorFlow's zero-cost accessibility suits enterprises seeking an economical, robust solution for their machine learning projects.

"The solution is free."
"It is an open-source solution, so anyone can use it free of charge."
"I rate TensorFlow's pricing a five out of ten."

Service and Support

TensorFlow customer service and support are rarely needed due to comprehensive documentation and strong community backing. Users primarily find solutions through extensive online resources, Stack Overflow, and GitHub issues. Queries posted on official platforms often receive timely and helpful responses. Users frequently rely on TensorFlow's documentation and community resources for problem-solving, while direct technical assistance remains largely unused.

These insights are based on the in-depth reviews provided by peers to help you make a better buying decision.

Download our TensorFlow Buyer's Guide for additional reliable information.