Google Vertex AI vs TensorFlow comparison

Read 19 TensorFlow reviews

3,647 Views
3,647 Comparison Views

100% willing to recommend

Google Vertex AI

Comparison Buyer's Guide

Download the report

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: December 2025).

Google Vertex AI vs. TensorFlow

December 2025

Download the complete report

Helped 881,665 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

Google Vertex AI

Ranking in AI Development Platforms

3rd

Average Rating

8.2

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

AI-Agent Builders (5th)

TensorFlow

Ranking in AI Development Platforms

8th

Average Rating

8.8

Reviews Sentiment

7.4

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of February 2026, in the AI Development Platforms category, the mindshare of Google Vertex AI is 8.1%, down from 16.1% compared to the previous year. The mindshare of TensorFlow is 5.4%, up from 3.9% 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	8.1%
TensorFlow	5.4%
Other	86.5%

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.

Read full review

Ton Jacobs

Owner at Go knowledge

Has good stability, but the process of creating models could be more user-friendly

The platform integrates well with other tools, especially Python, which we use to create models. These models can be deployed on mobile devices, which perfectly suits our requirements. It supports our AI-driven initiatives very well by producing AI models, which is its primary function. I recommend it for those seeking specialized scripting. However, it's important to consider other options as well. It is better suited for specialists in the field and is less user-friendly than general tools like Excel. I rate it overall at six out of ten. While it is a powerful tool, other software options are slightly simpler for training 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

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

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

"The best feature of Google Vertex AI is the ease of use, along with the integration with the rest of the Google ecosystem and the way models can be made available outside Google through endpoints."

"Vertex AI possesses multiple libraries, so it eliminates the need for extensive coding."

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

"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 training machine learning models. The AI model registry in Vertex AI is crucial for cataloging and managing various versions of the models we develop. When it comes to deploying models, we rely on Google Cloud's AI Prediction service, seamlessly integrating it into our workflow for real-time predictions or streaming. For monitoring and tracking the outcomes of model development, we employ Vertex AI Monitoring, ensuring a comprehensive understanding of the model's performance and results. This integrated approach within Vertex AI provides a unified platform for managing, deploying, and monitoring machine learning models efficiently."

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

"With just one single platform, Google Vertex AI platform, we can achieve everything; we need not switch over to multiple tools, multiple platforms, as everything can be accomplished through this one single platform for integration with existing workflows, systems, tools, and databases."

More Google Vertex AI pros

"It is open-source, and it is being worked on all the time. You don't have to pay all the big bucks like Azure and Databricks. You can just use your local machine with the open-source TensorFlow and create pretty good models."

"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."

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

"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."

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

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

More TensorFlow pros

Cons

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

"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."

"It would be beneficial to have certain features included in the future, such as image generators and text-to-speech solutions."

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

"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."

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

More Google Vertex AI cons

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

"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."

"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."

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

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

More TensorFlow cons

Pricing and Cost Advice

"The solution's pricing is moderate."

"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 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."

"The price structure is very clear"

"We are using the free version."

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

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

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

"The solution is free."

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

"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."

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

881,665 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Computer Software Company

12%

Financial Services Firm

Manufacturing Company

Educational Organization

Manufacturing Company

14%

Comms Service Provider

University

Financial Services Firm

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

I purchased Google Vertex AI directly from Google, as we are a partner of Google. I would rate the pricing for Google Vertex AI as low; the price is affordable.

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 is your primary use case for Google Vertex AI?

We are developing AI models and agents using Google Vertex AI platform, and we are deploying them using Google Vertex AI platform on Google Cloud Platform, GCP. With just one single platform, Googl...

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...

What is your primary use case for TensorFlow?

I've used TensorFlow for image classification tasks, object detection tasks, and OCR.

Azure OpenAI vs Google Vertex AI

Comparisons

Compared 20% of the time

Microsoft Azure Machine Learning Studio vs Google Vertex AI

Compared 13% of the time

Hugging Face vs Google Vertex AI

Compared 9% of the time

Amazon Bedrock vs Google Vertex AI

Compared 6% of the time

NVIDIA DGX Cloud vs Google Vertex AI

Compared 3% of the time

More Google Vertex AI Competitors

PyTorch vs TensorFlow

Compared 35% of the time

Hugging Face vs TensorFlow

Compared 10% of the time

OpenVINO vs TensorFlow

Compared 10% of the time

Azure OpenAI vs TensorFlow

Compared 7% of the time

Microsoft Azure Machine Learning Studio vs TensorFlow

Compared 6% of the time

More TensorFlow Competitors

Product Reports

Download Google Vertex AI product report

Google Vertex AI

January 2026

Download TensorFlow product report

January 2026

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