Google Cloud AI Platform vs TensorFlow comparison

Read 19 TensorFlow reviews

3,801 Views
3,801 Comparison Views

100% willing to recommend

Google Cloud AI Platform

Comparison Buyer's Guide

Download the report

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: July 2025).

Google Cloud AI Platform vs. TensorFlow

July 2025

Download the complete report

Helped 865,295 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 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 August 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 5.2%, down from 5.6% 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.

Read full review

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

"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"

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

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

"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."

"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."

"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."

More Google Cloud AI Platform 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."

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

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

"Edge computing has some limited resources but TensorFlow has been improving in its features. It is a great tool for developers."

"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 is easy to use and learn."

"The available documentation is extensive and helpful."

"Optimization is very good in TensorFlow. There are many opportunities to do hyper-parameter training."

More TensorFlow pros

Cons

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

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

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

"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."

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

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

More Google Cloud AI Platform cons

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

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

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

"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."

"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."

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

"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."

More TensorFlow cons

Pricing and Cost Advice

"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."

"The pricing is on the expensive side."

"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."

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

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

"We are using the free version."

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

"The solution is free."

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

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

865,295 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Computer Software Company

15%

Financial Services Firm

11%

Manufacturing Company

University

Manufacturing Company

15%

Computer Software Company

11%

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...

IBM Watson Machine Learning vs Google Cloud AI Platform

Comparisons

Compared 29% of the time

Azure OpenAI vs Google Cloud AI Platform

Compared 20% of the time

Hugging Face vs Google Cloud AI Platform

Compared 14% of the time

Distyl AI vs Google Cloud AI Platform

Compared 6% of the time

Microsoft Azure Machine Learning Studio vs Google Cloud AI Platform

Compared 5% of the time

More Google Cloud AI Platform Competitors

PyTorch vs TensorFlow

Compared 29% of the time

OpenVINO vs TensorFlow

Compared 14% of the time

Hugging Face vs TensorFlow

Compared 12% of the time

Google Vertex AI vs TensorFlow

Compared 10% of the time

MXNet vs TensorFlow

Compared 3% of the time

More TensorFlow Competitors

Product Reports

Download Google Cloud AI Platform product report

AI Development Platforms

August 2025

Download TensorFlow product report

July 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.

Sample Customers

Carousell

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