Google Vertex AI vs TensorFlow comparison

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Google Vertex AI

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Executive SummaryUpdated on Dec 4, 2024

TensorFlow and Google Vertex AI are products competing in machine learning solutions. Google Vertex AI seems to have the upper hand due to its integrated approach, which enhances efficiency and ease of use.

Features: TensorFlow offers flexibility and powerful support for neural networks with seamless adaptability across platforms. It supports a wide range of models and extensive documentation which is crucial for deep customization. Google Vertex AI provides an integrated suite for machine learning workflows from data preparation to model deployment. It enhances scalability and AI development efficiency thanks to its Google Cloud integration.

Room for Improvement: TensorFlow can be challenging in terms of manual setup and requires extensive technical knowledge. It might lack ease of integration outside the Google ecosystem. Google Vertex AI has a higher cost which might deter smaller businesses. Simplifying the initial setup process and reducing reliance on the Google Cloud infrastructure could enhance its attractiveness.

Ease of Deployment and Customer Service: Google Vertex AI provides seamless deployment due to its integration with the Google Cloud ecosystem, simplifying scaling and deployment with robust customer support. TensorFlow, while offering customization, often requires more effort in setup and integration, demanding higher technical skills for optimal use.

Pricing and ROI: TensorFlow's open-source nature eliminates initial licensing fees, offering potentially higher ROI depending on project requirements. Google Vertex AI entails higher costs due to its integrated services but balances this with efficient full-cycle AI development, appealing to those prioritizing rapid time-to-market and streamlined processes.

To learn more, read our detailed Google Vertex AI vs. TensorFlow Report (Updated: September 2025).

Google Vertex AI vs. TensorFlow

September 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

Google Vertex AI

Ranking in AI Development Platforms

2nd

Average Rating

8.2

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

AI-Agent Builders (4th)

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 Google Vertex AI is 10.3%, down from 19.5% 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 (%)
Google Vertex AI	10.3%
TensorFlow	6.6%
Other	83.1%

AI Development Platforms

Featured Reviews

Hamada Farag

Technology Consultant at Beta Information Technology

Customization and integration empower diverse AI applications

We are familiar with most Google Cloud services, particularly infrastructure services, storage, compute, AI tools, containerization, GCP containerization, and cloud SQL. We are familiar with approximately eighty percent of Google's services, primarily related to infrastructure, AI, containers, backup, storage, and compute. We are familiar with Gemini AI and Google Vertex AI, and we have completed some exercises and cases with our customers for Google AI. We use automation in machine learning. I work with a team where everyone has specific responsibilities. We have design and development processes in place. Based on my experience, I would rate Google Vertex AI a 9 out of 10.

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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.

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

"Google Vertex AI is an out-of-the-box and very easy-to-use solution."

"Vertex comes with inbuilt integration with GCP for data storage."

"The most useful function of Google Vertex AI for me is the ease of integration, as we can easily create a prompt and integrate it into our current system."

"The monitoring feature is a true life-saver for data scientists. I give it a ten out of ten."

"The most valuable feature we've found is the model garden, which allows us to deploy and use various models through the provided endpoints easily."

"Google Vertex AI is better for deployment, configuration, delivery, licensing, and integration compared to other AI platforms."

"It provides the most valuable external analytics."

"The integration of AutoML features streamlines our machine-learning workflows."

More Google Vertex AI pros

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

"The available documentation is extensive and helpful."

"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."

"It is also totally Open-Source and free. Open-source applications are not good usually. but TensorFlow actually changed my view about it and I thought, "Look, Oh my God. This is an open-source application and it's as good as it could be." I learned that TensorFlow, by sharing their own knowledge and their own platform with other developers, it improved the lives of many people around the globe."

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

"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."

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

More TensorFlow pros

Cons

"Both major systems, Azure and Google, are not yet stabilized, especially their customer support."

"The tool's documentation is not good. It is hard."

"The solution is stable, but it is quite slow. Maybe my data is too large, but I think that Google could improve Vertex AI's training time."

"I've noticed that using chat activity often presents a broader range of options and insights for a well-constructed question. Improving the knowledge base could be a key aspect for enhancement—expanding the information sources to enhance the generation process."

"I'm not sure if I have suggestions for improvement."

"It is not completely mature and needs some features and functions. The interface needs to be more user-friendly."

"It takes a considerable amount of time to process, and I understand the technology behind why it takes this long, but this is something that could be reduced."

"I think the technical documentation is not readily available in the tool."

More Google Vertex AI cons

"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."

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

"Personally, I find it to be a bit too much AI-oriented."

"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."

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

"There are a lot of problems, such as integrating our custom code. In my experience model tuning has been a bit difficult to edit and tune the graph model for best performance. We have to go into the model but we do not have a model viewer for quick access."

"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."

"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."

More TensorFlow cons

Pricing and Cost Advice

"The Versa AI offers attractive pricing. With this pricing structure, I can leverage various opportunities to bring value to my business. It's a positive aspect worth considering."

"I think almost every tool offers a decent discount. In terms of credits or other stuff, every cloud provider provides a good number of incentives to onboard new clients."

"The price structure is very clear"

"The solution's pricing is moderate."

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

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

"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."

"The solution is free."

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

"We are using the free version."

"TensorFlow is free."

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Top Industries

By visitors reading reviews

Computer Software Company

14%

Financial Services Firm

10%

Manufacturing Company

Educational Organization

Manufacturing Company

14%

Computer Software Company

11%

Financial Services Firm

University

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	5
Midsize Enterprise	3
Large Enterprise	7

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

Questions from the Community

What do you like most about Google Vertex AI?

We extensively utilize Google Cloud's Vertex AI platform for our machine learning workflows. Specifically, we leverage the IO branch for EDA data in Suresh Live Virtual, employing Forte IT for trai...

What is your experience regarding pricing and costs for Google Vertex AI?

They have different pricing models like pay-as-you-go or subscription model, and total cost of ownership. It is comparatively cheaper than Azure.

What needs improvement with Google Vertex AI?

We used AutoML feature for developing AI models automatically, but we are not comfortable with the performance of those models. We have to do some fine-tuning, hyperparameter optimization, and othe...

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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Product Reports

Download Google Vertex AI product report

Google Vertex AI

October 2025

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October 2025

Overview

Build, deploy, and scale ML models faster, with pre-trained and custom tooling within a unified artificial intelligence platform.

Google

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