Google Cloud AI Platform vs TensorFlow comparison

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Google Cloud AI Platform

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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

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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

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

"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."

"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."

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

"Some of the valuable features are the vast amount of services that are available, such as load balancer, and the AI architecture."

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

"The platform's Google Vision API is particularly valuable."

More Google Cloud AI Platform pros

"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 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."

"The available documentation is extensive and helpful."

"TensorFlow is a framework that makes it really easy to use for deep learning."

"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."

"Google is behind TensorFlow, and they provide excellent documentation. It's very thorough and very helpful."

"It is easy to use and learn."

"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."

More TensorFlow pros

Cons

"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."

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

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

"The technical support from Google is not very fast. I think it is about a five out of ten even though they have courses online where I can learn a lot, if I really need support, I have to wait a very long time."

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

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

"Improvements in text extraction accuracy and pricing adjustments would be helpful."

More Google Cloud AI Platform cons

"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."

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

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

"It currently offers inbuilt functions, however, having the ability to implement custom libraries would enhance its usefulness for enterprise-level applications."

"We encountered version mismatch errors while using the product."

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

"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 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."

More TensorFlow cons

Pricing and Cost Advice

"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."

"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 solution is free."

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

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

"TensorFlow is free."

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

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

"We are using the free version."

"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."

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Top Industries

By visitors reading reviews

Computer Software Company

16%

Financial Services Firm

10%

Manufacturing Company

University

Manufacturing Company

15%

Computer Software Company

12%

Financial Services Firm

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

AI Development Platforms

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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.

Sample Customers

Carousell

Airbnb, NVIDIA, Twitter, Google, Dropbox, Intel, SAP, eBay, Uber, Coca-Cola, Qualcomm

Google Cloud AI Platform vs. TensorFlow