Google Cloud AI Platform vs TensorFlow comparison

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Executive SummaryUpdated on Dec 4, 2024

TensorFlow and Google Cloud AI Platform operate in the machine learning and AI domain. Google Cloud AI Platform holds a stronger position due to its comprehensive cloud integration and enterprise-level deployment capabilities.

Features: TensorFlow offers flexible architecture, deep learning libraries, and compatibility with multiple CPUs and GPUs. Google Cloud AI Platform provides end-to-end machine learning services, strong Google Cloud integration, and automated scalable deployment.

Room for Improvement: TensorFlow can improve ease of integration and deployment processes for less experienced developers. It could enhance community support and documentation quality. Google Cloud AI Platform might reduce cost and expand the range of machine learning frameworks it supports, offering more flexibility and options outside Google’s ecosystem.

Ease of Deployment and Customer Service: TensorFlow may require extensive setup, best suited for developers familiar with a local environment, while Google Cloud AI Platform streamlines deployment with straightforward integration into existing cloud management systems, supported by responsive customer service.

Pricing and ROI: TensorFlow's open-source nature eliminates initial costs, offering a budget-friendly option for independent developers. Its ROI depends on user efficiency. Google Cloud AI Platform requires investment in cloud services, but its streamlined deployment and enhanced productivity often yield higher ROI in enterprise scenarios.

To learn more, read our detailed Google Cloud AI Platform vs. TensorFlow Report (Updated: June 2025).

Google Cloud AI Platform vs. TensorFlow

June 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 Cloud AI Platform

Ranking in AI Development Platforms

9th

Average Rating

7.8

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 July 2025, in the AI Development Platforms category, the mindshare of Google Cloud AI Platform is 3.6%, down from 6.4% compared to the previous year. The mindshare of TensorFlow is 4.4%, down from 6.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.

AI Development Platforms

Featured Reviews

HaiPham

Managing Director at sea-solutión

Helps with text extraction from images and has a straightforward setup process

We use Google Cloud AI Platform to extract text from images, such as forms. The platform's Google Vision API is particularly valuable. It helps with text extraction from images. Improvements in text extraction accuracy and pricing adjustments would be helpful. The solution is stable. We have…

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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.

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Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"On GCP, we are exposing our API services to our clients so that they send us their information. It can be single individual records or it can be a batch of their clients."

"Since the model could be trained in just a couple of hours and deploying it took only a few minutes, the entire process took less than an hour."

"The solution is able to read 90% of the documents correctly with a 10% error rate."

"I have seen measurable benefits from Google Cloud AI Platform."

"The initial setup is very straightforward."

"A range of a a wide range of algorithms, EIM voice mails, which can be plugged in right away into your solution into into into our solution, and then have platform that provides know, to to come up with an operational solution really quick."

"I think the user interface is quite handy, and it is easy to use as compared to the other cloud platforms."

"The feedback left about these tools was really helpful and informative for us"

More Google Cloud AI Platform pros

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

"The available documentation is extensive and helpful."

"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."

"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."

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

"I would rate the solution an eight out of ten. I am not a developer but more of an account manager. I can find what I want with TensorFlow. I haven’t contacted technical support for any issues. Since TensorFlow is vastly documented on the internet, I usually find some good websites where people exchange their views about the solution and apply that."

"It provides us with 35 features like patch normalization layers, and it is easy to implement using the Kras library when the Kaspersky flow is running behind it."

"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."

More TensorFlow pros

Cons

"One thing that I found is that Azure ML does not directly provide you with features on Google Cloud AI Platform, whereas Vertex provides some features of the platform."

"The initial setup was straightforward for me but could be difficult for others."

"I think it's the it it also has has evolved quite a bit over the last few years, and Google Cloud folks have been getting, more and more services. But I think from a improvement standpoint, so maybe they can look at adding more algorithms, so adding more AI algorithms to their suite."

"The model management on Google Cloud AI Platform could be better."

"Customizations are very difficult, and they take time."

"At first, there were only the user-managed rules to identify the best attributes of the individual. Then, we came up with a truth set and developed different machine learning models with the help of that truth set, so now it's completely machine learning."

"The solution can be improved by simplifying the process to make your own models."

"It could be more clear, and sometimes there are errors that I don't quite understand."

More Google Cloud AI Platform cons

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

"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."

"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."

"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."

"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."

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

"TensorFlow Lite only outputs to C."

More TensorFlow cons

Pricing and Cost Advice

"The solution has an attractive starting program, which costs only 300 USD for a duration of three months. During this period, one can accomplish a lot of work on the solution."

"The licenses are cheap."

"The pricing is on the expensive side."

"The price of the solution is competitive."

"For every thousand uses, it is about four and a half euros."

"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."

"The solution is free."

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

"We are using the free version."

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

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

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

"TensorFlow is free."

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Top Industries

By visitors reading reviews

Computer Software Company

16%

Financial Services Firm

11%

Manufacturing Company

10%

University

Manufacturing Company

15%

Computer Software Company

11%

Financial Services Firm

10%

University

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

What do you like most about Google Cloud AI Platform?

A range of a a wide range of algorithms, EIM voice mails, which can be plugged in right away into your solution into into into our solution, and then have platform that provides know, to to come up...

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

For the most part, the pricing is perfect sinceit grows with the use of my app. In some cases, they could be more specific about the pricing, especially for some AI features.

What is your primary use case for Google Cloud AI Platform?

I use Google Cloud AI Platform due to Firebase and the many APIs that are available with it.

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

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AI Development Platforms

July 2025

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June 2025

Overview

Google AI Platform is a managed service that enables you to easily build machine learning models, that work on any type of data, of any size. Create your model with the powerful TensorFlow framework that powers many Google products, from Google Photos to Google Cloud Speech.

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.

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