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: January 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

7th

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 February 2025, in the AI Development Platforms category, the mindshare of Google Cloud AI Platform is 4.9%, down from 7.0% compared to the previous year. The mindshare of TensorFlow is 3.9%, down from 8.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.

AI Development Platforms

Featured Reviews

Vipul-Kumar

CTO at Intelli AI LLC

An AI platform AI Platform to train your machine learning models at scale, to host your trained model in the cloud, and to use your model to make predictions about new data

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.

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Ashish Upadhyay

Founder at BlockMosiac

A robust tools for model visualization and debugging with superior scalability and stability, and an intuitive user-friendly interface

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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Quotes from Members

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

Pros

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

"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."

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

"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."

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

"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."

More Google Cloud AI Platform pros

"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 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."

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

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

"The available documentation is extensive and helpful."

"What made TensorFlow so appealing to us is that you could run it on a cluster computer and on a mobile device."

"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."

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

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."

"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."

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

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

"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 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."

More Google Cloud AI Platform cons

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

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

"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."

"In terms of improvement, we always look for ways they can optimize the model, accelerate the speed and the accuracy, and how can we optimize with our different techniques. There are various techniques available in TensorFlow. Maintaining accuracy is an area they should work on."

"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."

"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."

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

More TensorFlow cons

Pricing and Cost Advice

"The pricing is on the expensive side."

"The price of the solution is competitive."

"The licenses are cheap."

"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."

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

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

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

"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."

"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 an open-source solution, so anyone can use it free of charge."

More TensorFlow pricing and cost advice

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Top Industries

By visitors reading reviews

Computer Software Company

15%

Financial Services Firm

11%

Manufacturing Company

10%

University

Manufacturing Company

14%

Computer Software Company

12%

University

10%

Educational Organization

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

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

Google Cloud AI Platform vs. TensorFlow