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Caffe vs Microsoft Azure Machine Learning Studio vs TensorFlow comparison

 

Comparison Buyer's Guide

Executive Summary

Review summaries and opinions

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

Mindshare comparison

As of June 2025, in the AI Development Platforms category, the mindshare of Caffe is 0.3%, up from 0.1% compared to the previous year. The mindshare of Microsoft Azure Machine Learning Studio is 6.5%, down from 12.3% compared to the previous year. The mindshare of TensorFlow is 3.9%, down from 6.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.
AI Development Platforms
 

Featured Reviews

RL
Speeds up the development process but needs to evolve more to stay relevant
In the future, they should expand text processing, for a recommendation system, or to support some other models as well — that would be great. The concept of Caffe is a little bit complex because it was developed and based in C++. They need to make it easier for a new developer, data scientist, or a new machine or deep learning engineer to understand it. You can't work with metrics and vectors as Python does. Python is a vector-oriented language, but Caffe is not. When you deal with memory in C++, you have to allocate the data you will use in memory. You have to manage everything in C++. Conversely, in Python, you don't need to do that since everything is abstract and done by Python itself. It depends on every use case or your requirement goals. Some clients will require you to use Caffe because maybe their projects are old and they want to continue with Caffe. Others are comfortable with their current situation or they are afraid of migrating to another library. From my point of view, they need to make it easier for a new developer to use it. They should incorporate Python API to make it richer, overall.
Takayuki Umehara - PeerSpot reviewer
Streamlined workflows with drag and drop convenience but needs enhancements in AI
I use Machine Learning Studio for system reselling and integration Machine Learning Studio is easy to use, with a significant feature being the drag and drop interface that enhances workflow without any complaints. It provides a return on investment and cost savings, proving beneficial for…
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

"Caffe has helped our company become up-to-date in the market and has helped us speed up the development process of our projects."
"It's good for citizen data scientists, but also, other people can use Python or .NET code."
"The solution facilitates our production."
"Machine Learning Studio is easy to use."
"It's easy to use."
"Azure's AutoML feature is probably better than the competition."
"It helps in building customized models, which are easy for clients to use​.​​"
"The integration with Azure services enhances workflow and meets my expectations."
"Microsoft Azure Machine Learning Studio is easy to use and deploy."
"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."
"Edge computing has some limited resources but TensorFlow has been improving in its features. It is a great tool for developers."
"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."
"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."
"Optimization is very good in TensorFlow. There are many opportunities to do hyper-parameter training."
"TensorFlow is an efficient product for building neural networks."
"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."
 

Cons

"The concept of Caffe is a little bit complex because it was developed and based in C++. They need to make it easier for a new developer, data scientist, or a new machine or deep learning engineer to understand it."
"Overall, the icons in the solution could be improved to provide better guidance to users. Additionally, the setup process for the solution could be made easier."
"When you use different Microsoft tools, there are different pricing metrics. It doesn't make sense. The pricing metrics are quire difficult to understand and should be either clarified or simplified. It would help us sell the solution to customers."
"Using the solution requires some specific learning which can take some time."
"This solution could be improved if they could integrate the data pipeline scheduling part for their interface."
"In the Machine Learning Studio, particularly the Designer part, which is essentially Azure's demo designer, there is room for improvement. Many customers and users tend to switch to Microsoft Azure Multi-Joiners, which is a more basic version, but they do so internally. One area that could use enhancement is the process of connecting components. Currently, every time you want to connect a component, such as linking it to your storage or an instance like EC2, you have to input your username and password repeatedly. This can be quite cumbersome. Google, for instance, has made it more user-friendly by allowing easy access for connecting services within a workspace. In a workspace, you can set up various resources like storage, a database cluster, machine learning studio, and more. When connecting these services, there's no need to enter your username and password each time, making it a more efficient process. Another aspect to consider is the role of the designer, and they were to integrate a large language model to handle various tasks, it could significantly enhance the overall scalability and usability of the platform."
"If you want to be able to deploy your tools outside of Microsoft Azure, this is not the best choice."
"The solution should be more customizable. There should be more algorithms."
"The AutoML feature is very basic and they should improve it by using a more robust algorithm."
"For newcomers to the field, the learning curve can be steep, often requiring about a year of dedicated effort."
"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 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."
"There are connection issues that interrupt the download needed for the data sets. We need to prepare them ourselves."
"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."
"We encountered version mismatch errors while using the product."
"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."
"It would be nice if the solution was in Hungarian. I would like more Hungarian NAT models."
 

Pricing and Cost Advice

Information not available
"There isn’t any such expensive costs and only a standard license is required."
"To use MLS is fairly cheap. Even the paid account is something like $20/month, unless you are provisioning large numbers of VMs for a Hadoop cluster. The main MS makes money with this solution is forcing the user to deploy their model on REST API, and being charged each time the API is accessed. There are several pricing tiers for the API. If you do not use the API, then value of MLS is to create rapid experiments ($20/month). The resulting model is not exportable to use, thus you’ll have to recreate the algorithms in either R or Python, which is what I did. MLS results gave me a direction to work with, the actual work is mostly done in R and Python outside of MLS."
"ML Studio's pricing becomes a numbers game."
"The product's pricing is reasonable."
"The solution operates on a pay-per-use model."
"There is a license required for this solution."
"From a developer's perspective, I find the price of this solution high."
"It is less expensive than one of its competitors."
"I am using the open-source version of TensorFlow and it is free."
"It is an open-source solution, so anyone can use it free of charge."
"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."
"I rate TensorFlow's pricing a five out of ten."
"TensorFlow is free."
"We are using the free version."
"The solution is free."
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Top Industries

By visitors reading reviews
No data available
Financial Services Firm
13%
Computer Software Company
10%
Manufacturing Company
10%
Healthcare Company
6%
Manufacturing Company
15%
Computer Software Company
12%
Financial Services Firm
9%
University
8%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
 

Questions from the Community

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Which do you prefer - Databricks or Azure Machine Learning Studio?
Databricks gives you the option of working with several different languages, such as SQL, R, Scala, Apache Spark, or ...
What do you like most about Microsoft Azure Machine Learning Studio?
The learning curve is very low. Operationalizing the model is also very easy within the Azure ecosystem.
What is your experience regarding pricing and costs for Microsoft Azure Machine Learning Studio?
Pricing is considered to be top-segment and should be improved. I rate the pricing as three or four on a scale of one...
What do you like most about TensorFlow?
It empowers us to seamlessly create and deploy machine learning models, offering a versatile solution for implementin...
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 currentl...
 

Also Known As

No data available
Azure Machine Learning, MS Azure Machine Learning Studio
No data available
 

Overview

 

Sample Customers

Information Not Available
Walgreens Boots Alliance, Schneider Electric, BP
Airbnb, NVIDIA, Twitter, Google, Dropbox, Intel, SAP, eBay, Uber, Coca-Cola, Qualcomm
Find out what your peers are saying about Microsoft, Google, Hugging Face and others in AI Development Platforms. Updated: June 2025.
855,752 professionals have used our research since 2012.