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Azure AI Foundry vs Caffe 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:
 

Categories and Ranking

Azure AI Foundry
Ranking in AI Development Platforms
6th
Average Rating
8.0
Reviews Sentiment
5.7
Number of Reviews
16
Ranking in other categories
Low-Code Development Platforms (10th), Integration Platform as a Service (iPaaS) (11th), AI-Agent Builders (3rd)
Caffe
Ranking in AI Development Platforms
28th
Average Rating
7.0
Reviews Sentiment
6.0
Number of Reviews
1
Ranking in other categories
No ranking in other categories
 

Featured Reviews

Sudhakar Pyndi - PeerSpot reviewer
Data, Analytics & Ai Senior Director, Enterprise Architecture at a comms service provider with 10,001+ employees
Document processing has accelerated contract reviews and enabled rapid development of AI-driven supply chain solutions
With regard to security, compliance, or governance features in Azure AI Foundry, this is something that we have started looking into, primarily using Microsoft Purview for our governance, data governance. There is this new module called DSPM for AI, and we are exploring it while trying to operationalize it with different policies and so forth, but we're still not where we want to be on the governance, AI governance side. It's a process and a path, and we are trying to work through that right now. Azure AI Foundry can be improved from the governance perspective, as a lot can be done. The promising part is the recent announcement on the Foundry control plane. A couple of days back, there was an announcement regarding it bringing in some of the gaps that were on the platform, so it's a really positive direction in terms of where it's going. More governance is what is lacking, but the control plane will really play a big role there.
RL
Machine/Deep Learning Engineer at UpWork Freelancer
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.

Quotes from Members

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

Pros

"The features of Azure AI Foundry that I appreciate the most include the model catalog and the capability to deploy all of these different models, especially now that they've added Anthropic, which was the big one that was missing."
"Azure AI Foundry has helped me reduce the time taken for AI app and agent development significantly because it takes over a lot of the infrastructure work of connecting to these models."
"With Document Intelligence, we just went through Foundry, enabled Document Intelligence, and we were able to get everything done in less than 90 days for the complete end-to-end solution we built on that."
"In my evaluation process, I found that Microsoft Azure AI Foundry is much more accessible compared to AWS on model selection and the capabilities of using Document Intelligence versus Textract were much better."
"The benefit of using Azure AI Foundry for the organization is saving time, so saving time and then making money—now that we have this, many people were not doing this because it took them so long to do that research, that has been fixed."
"What I appreciate most about Azure AI Foundry is that for a single agent flow, the platform provides the ability to build agents quickly, which demonstrates the ease of use for a single agent workflow."
"The biggest return on investment for me when using Azure AI Foundry is the savings in cost for implementing our own observability, visibility, evaluation, and building our own infrastructure to do proof of concepts."
"The features of Azure AI Foundry that I appreciate the most include the containment of all the agents and the ability to see all agents in a single dashboard and to have access to all of them from one portal."
"Caffe has helped our company become up-to-date in the market and has helped us speed up the development process of our projects."
"Caffe has helped our company become up-to-date in the market and has helped us speed up the development process of our projects."
 

Cons

"My experience with deploying Azure AI Foundry is that, at this point in time, given the limited capabilities available in Foundry, we built pro-code agents, hosted them, containerized them, and deployed them."
"In Azure AI Foundry, I did not see many tools inside to test it that can really help us. I wish there were more tooling."
"I would evaluate customer service and technical support as terrible. Whenever I have to reach out to customer support, I end up waiting sometimes days for a response, and our 24-hour response time often turns into three to five days."
"For Azure AI Foundry, there is no actual clear pricing structure, which can be confusing for customers to understand, as every feature that you activate has its own price, and it is not very clear sometimes to define the pricing."
"My experience with Azure AI Foundry's pricing, setup cost, and licensing was a mess."
"Azure AI Foundry can be improved from the governance perspective, as a lot can be done."
"The deployment process for Azure AI Foundry has been very challenging. There are many issues with policies, and because we have very stringent controls, we need to know exactly how Azure AI Foundry works before we can deploy it."
"I would improve Azure AI Foundry by adding more functions within Foundry itself, as right now it is quite basic in what it does."
"Personally, I don't recommend Caffe if you're looking for a scalable system."
"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."
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Top Industries

By visitors reading reviews
Manufacturing Company
14%
Outsourcing Company
12%
Financial Services Firm
12%
Construction Company
10%
No data available
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business2
Midsize Enterprise2
Large Enterprise13
No data available
 

Questions from the Community

What is your experience regarding pricing and costs for Azure AI Foundry?
I would need to ask my technical team about my experience with the pricing, setup costs, and licensing.
What needs improvement with Azure AI Foundry?
The platform's effect on my management of privacy, performance, and compliance across different regions is quite complex because Azure AI Foundry does not make it very clear how to deploy. We set u...
What is your primary use case for Azure AI Foundry?
My main use cases for Azure AI Foundry include deploying AI applications to perform document comparison, translation services, and a chat feature, helping the digital AI team at our company. Curren...
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