Azure AI Foundry vs Gemini Enterprise Agent Platform comparison

Azure AI Foundry vs. Gemini Enterprise Agent Platform

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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

Azure AI Foundry

Ranking in AI Development Platforms

6th

Ranking in AI-Agent Builders

3rd

Average Rating

8.0

Reviews Sentiment

5.7

Number of Reviews

Ranking in other categories

Low-Code Development Platforms (10th), Integration Platform as a Service (iPaaS) (12th)

Gemini Enterprise Agent Pla...

Ranking in AI Development Platforms

1st

Ranking in AI-Agent Builders

5th

Average Rating

8.2

Reviews Sentiment

6.3

Number of Reviews

Ranking in other categories

No ranking in other categories

Featured Reviews

Sudhakar Pyndi

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.

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Hamada Farag

Technology Consultant at Beta Information Technology

Customization and integration empower diverse AI applications

We are familiar with most Google Cloud services, particularly infrastructure services, storage, compute, AI tools, containerization, GCP containerization, and cloud SQL. We are familiar with approximately eighty percent of Google's services, primarily related to infrastructure, AI, containers, backup, storage, and compute. We are familiar with Gemini AI and Google Vertex AI, and we have completed some exercises and cases with our customers for Google AI. We use automation in machine learning. I work with a team where everyone has specific responsibilities. We have design and development processes in place. Based on my experience, I would rate Google Vertex AI a 9 out of 10.

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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

"The most beneficial feature of Azure AI Foundry for enhancing customer experience is the ability to use Azure Functions to call things outside of Azure AI Foundry, making it more comprehensive as a feature."

"Azure AI Foundry makes it very straightforward, as I do not have to write thousands of lines of code; I rely on GitHub Copilot and Azure AI Foundry."

"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."

"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."

"The most beneficial feature for enhancing our customer experience is that it is easy to use for them and for us to implement."

"The feature of Azure AI Foundry that I prefer most is the guardrails, as it is much easier than the one that Bedrock in AWS provides."

"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."

"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."

More Azure AI Foundry pros

"We extensively utilize Google Cloud's Vertex AI platform for our machine learning workflows. Specifically, we leverage the IO branch for EDA data in Suresh Live Virtual, employing Forte IT for training machine learning models. The AI model registry in Vertex AI is crucial for cataloging and managing various versions of the models we develop. When it comes to deploying models, we rely on Google Cloud's AI Prediction service, seamlessly integrating it into our workflow for real-time predictions or streaming. For monitoring and tracking the outcomes of model development, we employ Vertex AI Monitoring, ensuring a comprehensive understanding of the model's performance and results. This integrated approach within Vertex AI provides a unified platform for managing, deploying, and monitoring machine learning models efficiently."

"The most valuable feature we've found is the model garden, which allows us to deploy and use various models through the provided endpoints easily."

"Vertex comes with inbuilt integration with GCP for data storage."

"The features I have found most valuable in Google Vertex AI are Gemini's large language models, which are currently among the best, and the vision tool of Gemini, which I consider quite good."

"Google Vertex AI is an out-of-the-box and very easy-to-use solution."

"With just one single platform, Google Vertex AI platform, we can achieve everything; we need not switch over to multiple tools, multiple platforms, as everything can be accomplished through this one single platform for integration with existing workflows, systems, tools, and databases."

"The best feature of Google Vertex AI is the ease of use, along with the integration with the rest of the Google ecosystem and the way models can be made available outside Google through endpoints."

"Vertex AI possesses multiple libraries, so it eliminates the need for extensive coding."

More Gemini Enterprise Agent Platform pros

Cons

"I find that the online documentation can sometimes be confusing, requiring extensive searching through multiple articles to locate specific information."

"Right now, because of the UI and the complexity of it, it is complicated and cumbersome, and I think figuring out how to simplify that would probably make it a faster process."

"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."

"Azure AI Foundry could be improved with better integration within all the other tools from Microsoft."

"My experience with Azure AI Foundry's pricing, setup cost, and licensing was a mess."

"I would improve Azure AI Foundry by adding more functions within Foundry itself, as right now it is quite basic in what it does."

"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."

"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."

More Azure AI Foundry cons

"I believe that Vertex AI is a robust platform, but its effectiveness depends significantly on the domain knowledge of the developer using it. While Vertex AI does offer support through the console UI in the Google Cloud environment, it is better suited for technical members who have a deeper understanding of machine learning concepts. The platform may be challenging for business process developers (BPDUs) who lack extensive technical knowledge, as it involves intricate customization and handling numerous parameters. Effectively utilizing Vertex AI requires not only familiarity with machine learning frameworks like TensorFlow or PyTorch but also a proficiency in Python programming. The complexity of these requirements might pose challenges for less technically oriented users, making it crucial to have a solid foundation in both machine learning principles and Python coding to extract the full value from Vertex AI. It would be beneficial to have a streamlined process where we can leverage the capabilities of Vertex AI directly through the BigQuery UI. This could involve functionalities such as creating machine learning models within the BigQuery UI, providing a more user-friendly and integrated experience. This would allow users to access and analyze data from BigQuery while simultaneously utilizing Vertex AI to build machine learning models, fostering a more cohesive and efficient workflow."

"Both major systems, Azure and Google, are not yet stabilized, especially their customer support."

"Google Vertex AI is good in machine learning and AI, but it lacks optimization."

"The solution is stable, but it is quite slow. Maybe my data is too large, but I think that Google could improve Vertex AI's training time."

"It is not completely mature and needs some features and functions. The interface needs to be more user-friendly."

"I've noticed that using chat activity often presents a broader range of options and insights for a well-constructed question. Improving the knowledge base could be a key aspect for enhancement—expanding the information sources to enhance the generation process."

"We used AutoML feature for developing AI models automatically, but we are not comfortable with the performance of those models."

"I think the technical documentation is not readily available in the tool."

More Gemini Enterprise Agent Platform cons

Pricing and Cost Advice

Information not available

"I think almost every tool offers a decent discount. In terms of credits or other stuff, every cloud provider provides a good number of incentives to onboard new clients."

"The solution's pricing is moderate."

"The Versa AI offers attractive pricing. With this pricing structure, I can leverage various opportunities to bring value to my business. It's a positive aspect worth considering."

"The price structure is very clear"

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Top Industries

By visitors reading reviews

Manufacturing Company

13%

Outsourcing Company

12%

Financial Services Firm

11%

Construction Company

Financial Services Firm

10%

Computer Software Company

10%

Manufacturing Company

10%

Comms Service Provider

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	2
Midsize Enterprise	2
Large Enterprise	13

By reviewers
Company Size	Count
Small Business	5
Midsize Enterprise	4
Large Enterprise	7

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...

What is your experience regarding pricing and costs for Google Vertex AI?

I purchased Google Vertex AI directly from Google, as we are a partner of Google. I would rate the pricing for Google Vertex AI as low; the price is affordable.

What needs improvement with Google Vertex AI?

Google Vertex AI is quite complex to navigate and to start services with, as I need to do a lot of iterations to finally activate the services, which is one major flaw, although it is powerful. To ...

What is your primary use case for Google Vertex AI?

Google Vertex AI has been utilized for Vertex Pipelines. I have not utilized the pre-trained APIs in Google Vertex AI, as our deployment is primarily on AWS, and we use API calls.

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Product Reports

Azure AI Foundry

Download Azure AI Foundry product report

Gemini Enterprise Agent Platform

Download Gemini Enterprise Agent Platform product report

Also Known As

No data available

Vertex, Google Vertex AI

Overview

Azure AI Foundry harnesses advanced AI technologies to streamline complex tasks across industries, offering cutting-edge solutions that enhance business processes and boost efficiency.

Azure AI Foundry integrates seamlessly into business environments, leveraging AI to transform traditional operations. It supports diverse applications by providing robust machine learning capabilities that drive innovation and enable intelligent automation. Designed to handle large-scale data analytics, it empowers users to make data-driven decisions swiftly and accurately, thereby optimizing resources and workflows.

What are the key features of Azure AI Foundry?

Scalable Machine Learning: Provides tools to build, deploy, and manage machine learning models at scale.
Data Integration: Offers seamless integration with data storage solutions for comprehensive data processing.
Security and Compliance: Ensures high-level security measures and compliance standards for data handling.
Interactive AI Development: Facilitates interactive and collaborative development environments for AI projects.

What benefits and ROI should users look for when evaluating Azure AI Foundry?

Cost Efficiency: Reduces operational costs through automation and optimized resource usage.
Improved Decision-Making: Enhances decision-making with real-time insights and analytics.
Innovation Acceleration: Supports rapid deployment of AI solutions, driving technological advancement.
Enhanced Productivity: Boosts productivity by automating routine tasks, allowing focus on strategic activities.

Azure AI Foundry is utilized across multiple industries, including finance, healthcare, and manufacturing, where it aids in predictive analytics, patient data management, and supply chain optimization. Its ability to integrate AI-driven insights into everyday operations helps these sectors enhance their efficiency and innovate steadily in dynamic markets.

Microsoft

Gemini Enterprise Agent Platform offers advanced AI capabilities with large language and vision models, a seamless Google ecosystem integration, and low-code/no-code options, enhancing AI agent development, deployment, and monitoring.

Gemini Enterprise Agent Platform empowers businesses with its comprehensive AI development environment, focusing on generative and agentic AI applications. It streamlines tasks like chatbots, document summarization, and data workflows integration. Despite its complexity and high cost, it offers advantages in feature centralization, end-to-end integration, external analytics, and multi-model capabilities. Challenges include navigation issues, setup complexity, and documentation accessibility. Users wish for improved model efficiency, system integration, customer support, and a more user-friendly experience for non-technical users. The platform's potential in optimizing machine learning modules and managing fintech tasks makes it valuable for organizations seeking robust AI model management.

What are Gemini Enterprise Agent Platform's Key Features?

Large Language and Vision Models: Enhance AI capabilities across diverse applications.
Google Ecosystem Integration: Seamlessly integrates within Google's platforms.
Feature Store: Centralizes features for better team communication and model management.
Low-Code/No-Code Options: Facilitates AI development with minimal coding.
End-to-End Integration: Offers comprehensive tools for agent deployment and monitoring.

What Benefits and ROI Should Users Look For?

Feature Centralization: Streamlines communication and coordination across teams.
Integration Ease: Simple integration with existing systems enhances operational efficiency.
AI Optimization: Improves machine learning modules, reducing costs and boosting efficiency.
Advanced Language Analytics: Enables better data insights via keyword detection and document processing.

Gemini Enterprise Agent Platform finds application in industries focusing on generative AI and agentic tasks. Businesses use it for chatbots, document summarization, and fintech-related tasks, benefiting from its capabilities in handling large documents, keyword detection, and language analytics. Its ease of integration with current systems is a significant advantage for many organizations.

Google

Azure AI Foundry vs. Gemini Enterprise Agent Platform