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Google Cloud AI Platform vs TensorFlow comparison

 

Comparison Buyer's Guide

Executive Summary
 

Categories and Ranking

Google Cloud AI Platform
Ranking in AI Development Platforms
7th
Average Rating
7.8
Number of Reviews
8
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
19
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of November 2024, in the AI Development Platforms category, the mindshare of Google Cloud AI Platform is 7.3%, down from 7.5% compared to the previous year. The mindshare of TensorFlow is 5.7%, down from 9.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.
AI Development Platforms
 

Featured Reviews

Vipul-Kumar - PeerSpot reviewer
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.
Ashish Upadhyay - PeerSpot reviewer
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.

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."
"Some of the valuable features are the vast amount of services that are available, such as load balancer, and the AI architecture."
"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 platform's Google Vision API is particularly valuable."
"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."
"The initial setup is very straightforward."
"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 available documentation is extensive and helpful."
"It's got quite a big community, which is useful."
"Google is behind TensorFlow, and they provide excellent documentation. It's very thorough and very helpful."
"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."
"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."
"Optimization is very good in TensorFlow. There are many opportunities to do hyper-parameter training."
"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."
 

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."
"The initial setup was straightforward for me but could be difficult for others."
"Customizations are very difficult, and they take time."
"It could be more clear, and sometimes there are errors that I don't quite understand."
"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."
"TensorFlow Lite only outputs to C."
"It would be cool if TensorFlow could make it easier for companies like us to program for running it across different hyperscalers."
"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."
"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."
"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."
"The process of creating models could be more user-friendly."
"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."
"The solution is hard to integrate with the GPUs."
 

Pricing and Cost Advice

"The licenses are cheap."
"For every thousand uses, it is about four and a half euros."
"The pricing is on the expensive side."
"The price of the solution is competitive."
"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."
"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."
"It is an open-source solution, so anyone can use it free of charge."
"The solution is free."
"TensorFlow is free."
"We are using the free version."
"I am using the open-source version of TensorFlow and it is free."
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Top Industries

By visitors reading reviews
Computer Software Company
15%
Financial Services Firm
11%
Manufacturing Company
10%
University
9%
Manufacturing Company
15%
Computer Software Company
12%
University
10%
Educational Organization
10%
 

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 primary use case for Google Cloud AI Platform?
We use Google Cloud AI Platform to extract text from images, such as forms.
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 needs improvement with TensorFlow?
The process of creating models could be more user-friendly.
 

Learn More

Video not available
 

Overview

 

Sample Customers

Carousell
Airbnb, NVIDIA, Twitter, Google, Dropbox, Intel, SAP, eBay, Uber, Coca-Cola, Qualcomm
Find out what your peers are saying about Google Cloud AI Platform vs. TensorFlow and other solutions. Updated: October 2024.
816,406 professionals have used our research since 2012.