Microsoft Foundry Reviews

My main use case for Microsoft Foundry is to build AI agents and prompt flows.

A specific example of an AI project I built using Microsoft Foundry is a decision support tool that analyzed processes and capability metrics, and we cut down the decision turnaround time from three hours to 20 minutes.

A statistical analysis workflow that earlier took three hours was cut down to 20 minutes after using Microsoft Foundry.

Microsoft Foundry has positively impacted my studies because it demystified the agent building process for me.

In my opinion, the best features Microsoft Foundry offers include everything end-to-end from the agent building canvas to evaluation and guardrail controls, and the continuous evaluation of your agent or model.

These features make my work easier and more effective because, first of all, I don't need to worry about getting lost in the system. Microsoft Foundry provides a very interactive canvas where I can use drag-and-drop features, or the skeleton is already laid out for the agent. I just need to enter my prompts in the fields, such as for a user prompt or system prompt, or if I want to attach any tools, a simple click provides a one-click solution for that, and I can type in the link to my HTTP server and that will easily give me access to tools. I also appreciate how there was a separate section for skills and agent skills. The UI was intuitive, and I would not get lost in the software.

The AI guardrails feature allows me to upload a whole golden dataset of things I don't want, such as things I want to block the AI from producing or things that are allowed.

Microsoft Foundry could be improved if there was some way to integrate Azure Machine Learning Ops within it, so we can continually assess our agent's performance or our model's outputs and our custom machine learning model's output. That would be a great feature to have.

I gave it a seven because whenever I needed to build a multi-agent system, the prompt flow that would control the chain of command between the agents had to be built inside the AI Hub. The agents themselves were built in Microsoft Foundry, so I always needed to navigate between the two to customize something within the agents and then switch to Microsoft Foundry. When I needed to customize my entire system and the prompt flow, then I needed to navigate to the AI Hub. That was a bit cumbersome at times and led to confusion.

I have been using Microsoft Foundry for about a year now.

Regarding Microsoft Foundry's AI capabilities, I found the accuracy and reliability of output to be very reliable.

Microsoft Foundry is deployed in my organization as a hybrid cloud.

Azure AI Foundry has helped reduce the time taken for AI app and agent development cycles in my work significantly. Something that took two days can now be done in less than 24 hours.

My advice to others looking into using Microsoft Foundry is that it's a great product that comes with a great set of features. As someone starts using it, they'll realize how easy it is and discover that the interface is very intuitive. Anyone who spends time on the platform will discover the features themselves, and it's pretty self-explanatory. I gave this product a rating of seven out of ten.

My main use cases for Azure AI Foundry are developing agents that work with documents, including Q&A, RAG Q&A, and one that uses a code interpreter to work with data. I am developing agents and utilizing Azure Machine Learning for other use cases. I also utilize Azure AI Search.

Azure AI Foundry has helped reduce the time taken for AI app and agent development cycles by approximately 50% for one use case. What I appreciate most about Azure AI Foundry is the possibility to have everything in one place, including all the traceability, observability, the deployment of the agents, and the model catalog.

The most useful observability features are those for agents, allowing me to see step-by-step what the agent was doing and have an evaluation on that. Observability is the feature I value most.

The AI service I am realizing the most impact from is Document Intelligence. Document Intelligence impacts my company by processing every OCR and every document we use, as 70% is made using Document Intelligence to OCR the documents.

The most impactful use of AI Search for us is as a vector database for the indexes, where we have all our indexes in AI Search. AI Search impacts my company as it serves as the vector database and the central point for all the indexes, essentially being the central resource we use for indexing.

To improve Azure AI Foundry, I would integrate Copilot Studio and Azure AI Foundry because at the moment they are two different resources. What our customers ask for is one resource that brings together Copilot and Foundry, and that is what is missing now for a unified experience.

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.

I have been using Azure AI Foundry for approximately one year.

The stability and reliability of Azure AI Foundry platform are fine, but we have not used it with thousands of users at the moment.

It is probably too soon to tell how well it scales. At the moment, as I mentioned, we use Azure AI Foundry for proof of concept, and for scaling, we utilize Kubernetes and other solutions.

The customer service and technical support from Microsoft were great, especially considering it is a service that has just been released.

Positive

I was not using another solution similar to Azure AI Foundry before choosing it; we were using custom solutions or AI Studio.

The biggest return on investment for me when using Azure AI Foundry is the reduction of time, and to understand the ROI, it is possible to understand it by the time reduction. It is not possible to get it by the decentralization that Azure AI Foundry provides, so these are the two main reasons. Azure AI Foundry has been able to reduce the time for one case by half, going from six weeks of development to three weeks.

I did not consider another solution while using Azure AI Foundry because we are a Microsoft partner, so we are essentially required to use it.

We actually do not use much security functionality on Azure AI Foundry, so I think the question does not apply.

My experimentation and differentiation efforts are affected by the fact that we mostly use OpenAI, so for our use case, we did not choose other partners such as Mistral, but it is valuable to have a comprehensive model catalog.

My advice to other companies considering Azure AI Foundry is to use it for prototyping and to try it out now with the latest releases, as they integrated a comprehensive way for telemetry and integration with API management. Azure AI Foundry is growing, so I suggest giving it a try. I would rate this solution a 7.5 out of 10.

My main use case for Microsoft Foundry is building chatbots.

A specific example of how I'm using Microsoft Foundry for my chatbot is that it functions as a front end for what we do, and then we have a chatbot and a search index all embedded in one.

I would like to add that perhaps in the future we would like to do more, depending on corporate standards or if something we're missing. There are nice to have things I would like to know or understand going forward.

Microsoft Foundry has positively impacted our organization as it has helped users find what they wanted using the chatbot that we built all in one place, which cut down a lot of the manual work.

The best features Microsoft Foundry offers are the cost and the ability to integrate with other things.

The cost and integration features have helped our team because we are a mid-scale company, so we are constantly looking for things to do at a decent price. This solution has given us the flexibility to do what we needed to do, or actually above and beyond with more than what we wanted at a decent price.

Regarding Microsoft Foundry's AI capabilities, I love its governance and security.

What I specifically appreciate about the governance and security features is the ability to do a Microsoft Entra ID integration without doing it manually, which saves us a lot of time.

Regarding Microsoft Foundry's AI capabilities, I would say its accuracy and reliability of output is around 70 to 80 percent based on what we have noticed.

I think Microsoft Foundry can be improved by providing integrations with things such as MCP, which is our next goal. It would be helpful to know if that is possible or how that can be done.

I have been using Microsoft Foundry for about a year.

Microsoft Foundry is stable.

I think Microsoft Foundry's scalability is good but can be improved.

The customer support for Microsoft Foundry is good.

I would rate the customer support on a scale of one to ten as a seven since we have not used it as much.

I did not previously use a different solution because this is our first.

My experience with pricing, setup cost, and licensing is that it is good and definitely manageable.

Before choosing Microsoft Foundry, we did not evaluate other options; somebody we know suggested this, and we went with it.

I would rate Microsoft Foundry an eight on a scale of one to ten.

I choose that number of eight because it has definitely helped us go above and beyond to where we are, but the reason I said not a ten is because we personally think we probably have not discovered what it can do.

My advice to others looking into using Microsoft Foundry is to go ahead and do it because it is worth it. I have provided this review with an overall rating of eight.

Microsoft Foundry's main use cases include integration, offering more than ten thousand connectors, MCP servers, APIs, and model graphs, including Fabric IQ. The platform also provides capabilities for retrieval augmented generation (RAG) applications for generative AI. Content retrieval filters and governance are easily accessible features.

In my daily use of Microsoft Foundry, I utilize it for customer support and services integrated with Microsoft 365 to handle tickets. The agents summarize customer history to assist support staff in taking the best actions. Microsoft Foundry agents act in Teams to automatically draft responses for customer queries and route complex cases to human agents. For report generation, scheduling, and approval, I interact with Microsoft Office tools, Copilot, and Teams.

For AI governance and tracking AI models in my daily work, I utilize centralized catalogs, which include many model catalogs such as Anthropic and OpenAI, and manage roles including role-based access control (RAC) and role workplace access. I can effectively manage generative AI model compliance while using filters to avoid bias and malicious content.

The best features Microsoft Foundry offers include AI model selection, agent development, governance, and deployment of automation and AI agents. It has model access and optimization features, with more than ten thousand models available from sources such as OpenAI, Meta, Google, and Hugging Face. I can fine-tune and control those models with minimal coding and utilize agent deployment through the Microsoft agent framework, serverless hosting, and one-click deployment.

The main features also include enterprise governance and security with Microsoft Intra agent ID, role-based access, and built-in observability tools such as dashboard tracking and monitoring. Microsoft Foundry's governance and security capabilities include a built-in observability dashboard for tracking, monitoring, and evaluation. Microsoft Intra agent ID facilitates centralized identity and policy enforcement, supported by role-based access control, content filters, network isolation, and safeguards against bias and malicious content. These enterprise governance and security offerings provide substantial value.

Suggestions for improving Microsoft Foundry relate to setup complexities. Achieving strong governance setups requires extensive configuration, particularly with role-based access and Azure policies, which can be costly for large-scale deployments. Managing costs for model routing, tuning, and the necessary training for teams adopting AI agent development pose challenges while ensuring compliance with organization regulations.

Initiating with small pilot or automation workflows for scalability would be beneficial. However, complexities such as change management surrounding costs for tweaking and tuning models, along with the complexity of role-based governance setups, need attention for better outcomes.

I have been using Microsoft Foundry for around three years.

Microsoft Foundry is stable based on my experience utilizing tools across Dynamics, Power Platform, and various connectors, providing reliable automation for customer feedback and accurate insights for Power BI dashboards.

I initially trialed on-premises applications and have since expanded to include cloud applications and external customer AI flows. Microsoft Foundry has adequately catered to my scalability needs and provided a positive experience, optimizing for user increases.

I have not reached out for customer support for Microsoft Foundry, as administration manages tasks related to pricing and support.

Before Microsoft Foundry, I used AI Builder capabilities along with Hugging Face and various AI models for agent development on the Microsoft platform, employing Microsoft Automate solutions prior to leveraging Microsoft Foundry.

Microsoft Foundry is initially deployed on-premises and will transition to a hybrid model. I have implemented an internal organizational chatbot and customer-facing AI agents, first starting from the on-premises setup.

I have conducted case studies regarding investment returns from Microsoft Foundry, evaluating pricing structures and AI model utilization. These metrics assist in pre-calculating time, effort savings, and reductions in staff needs, contributing to improved cost management outcomes.

Considering Microsoft Foundry, I evaluated options within the Microsoft ecosystem, including Power Platform, Dynamics 365, and Azure Cloud. My choice was influenced by organizational alignment with Microsoft technologies.

I make extensive use of Azure AI services, particularly Document Intelligence, which aids in reading and categorizing enterprise documents while extracting key information. Additionally, Azure Language and Speech services are utilized for transcribing meeting calls and extracting critical data from recorded interactions, proving to be notably impactful.

The increasing complexity of Microsoft technologies such as Power Apps, Dynamics 365, and Power APIs can lead to a need for distinct training and case studies to guide AI chatbot and agent development. These challenges become apparent when designing enterprise-scale agents or RAG applications for content generation.

I develop AI agents primarily for customer support and approval tasks, utilizing Foundry Agent Service effectively. This has streamlined agent deployment by leveraging knowledge bases and responding to queries efficiently. I look forward to enhancing RAG applications focused on generating responses for customers.

I employ RAG pipelines and various models including Hugging Face, which provide pre-trained transformers, enhancing my experimentation with different AI models. These versatile AI models increase efficiency, with model routing and effective prompt management proving to be beneficial.

Microsoft Foundry has significantly reduced development cycles for AI applications and agents. I started with smaller AI agents for approval flows and progressed to customer feedback and support, utilizing Microsoft 365 and developing dashboards for Power BI. The learning curve involves security setups and multi-agent workflow training, though it has shortened deployment times through Microsoft Foundry and SDK despite initial challenges.

I deploy AI applications on the cloud for customer support and generate queries. My AI applications encompass direct customer interactions through knowledge bases, ensuring that unresolved queries are routed to human assistance. Implementing AI applications on the cloud has facilitated compliance with safety regulations through role-based access and policies.

Change management is positively affected by AI agents in Teams, facilitating smooth change requests and deployment. Data security is enhanced by regulating standards and grounding responses from AI, which helps significantly with governance and security. Change management is evident when needing any modifications and ensuring adaptation for enterprise requirements, contributing to improved collaboration and workload reduction through AI assistance.

Since I started using Microsoft Foundry alongside Dynamics 365 and the Power platform, it has positively impacted my organization by utilizing pre-built connectors and Microsoft Graph grounding to create various Power BI dashboards. It helps generate AI agents as well as RAG applications for generative AI and supports multiple agent workflows, efficiently routing feedback to customer support teams. The AI agent responds to customer queries and resolves issues, which is beneficial through the Microsoft Agent Framework SDK, coupled with the available models for fine-tuning and performance optimization.

I advise those considering Microsoft Foundry to have a basic understanding of the Microsoft ecosystem, which streamlines implementation and feature utilization. Familiarity with tools including Dynamics 365 and Azure Cloud enhances experience with AI models and applications, making the learning curve more intuitive.

I rate Microsoft Foundry seven out of ten overall.

I am using Microsoft Foundry solution. Microsoft Foundry has different models that allow me to access various types of AI models. I am currently utilizing different types of AI models through these capabilities.

We are planning to integrate AI agents in the future. We are thinking about developing this capability as part of our roadmap.

We utilize Azure AI Search, RAG, and similar capabilities. When it comes to Azure search features, I find Azure Speech, Content Understanding, Document Intelligence, and Azure Language impactful in our use cases.

Regarding the observability features in Azure, I have found the observability features to be the most useful and impactful for our organization.

AI and Copilot features are something that everyone in the organization makes use of in place of prompts and documentation across all of Office 365. We are making use of these features on a daily basis in our work. The prompts, language base, and data analytics perspective are valuable to us. Additionally, the data exploration and reporting creation capabilities are beneficial.

For Power BI, we make use of it for business intelligence and reporting. The dashboard reporting and Copilot-generated insights inside Power BI are particularly valuable to our organization.

Microsoft Foundry can be improved and enhanced in the future in several ways. From a features and functionality perspective, there are missing features that I would like to see included or enhanced, apart from the consolidated product.

I have concerns about pricing. Although we currently are using a Microsoft Partner, the pricing model is a concern for us. The yearly changes in pricing help us plan our budget before entering a new fiscal year, and I find this acceptable because we can include it in our budget planning. However, if there were a way to reshuffle the payment structure to be more cost-effective based on user base, that would be beneficial.

I would appreciate if Microsoft could make an adjustment in the payment structure for each license and add-ons. If I want to pay for a particular license, there should be more beneficial features and value included inside rather than having to pay for multiple add-ons separately. Having one comprehensive license with everything included at once would work better for our organization than managing multiple add-ons. I understand that an all-in-one tool might be expensive and organizations may not use all the tools in a single license, but the current add-on model creates challenges.

I have known Microsoft for a while and have been working with Microsoft tools for approximately four years. I can really vouch for Microsoft, and that is why we make use of Microsoft. Microsoft is the best recommendation for me for any staff and any organization.

Microsoft Foundry has helped reduce the time taken for AI app and agent development cycles in our organization. I would recommend Microsoft to other organizations who are considering solutions for their environment.

Our main use case for Azure AI Foundry is that we built a chatbot and use Foundry as a control plane where we configured the content moderation and model precision, along with many guardrails.

The Azure AI Foundry feature that has been most beneficial for enhancing customer experience is the integration of Azure Purview with Foundry because AI is data, context, and intelligence. Now that we have this integration, I want to ensure I can tag the data and model throughout the AI life cycle.

I measure this improvement by looking at the deployment of the chatbot for an insurance customer, which was providing responses very slowly. We tried everything in Foundry, but it was not improving, so we deployed Redis Cache in front of it and made a copy of all the questions from the chatbot, which made the response faster. We made architectural changes within Foundry and integrated with Redis Cache to improve the speed of the chatbot. It's similar to how your phone with Maps works—if you stay in a hotel and look up the location once, the second time it automatically asks if you want to go there. We implemented caching before Foundry and the chatbot.

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 fact that it is integrated with Defender for Cloud allows me to see the metrics in Defender for Cloud, which is the CISP.

This feature benefits a company because I can get the posture of AI across the cloud, which was a missing part. People talk about the system, but I need to do the overall posture, which means infrastructure, apps, and context.

I can only assess the integration of Azure AI Foundry with existing cloud services for Azure. While I find it pretty simple, I think there is more that could be done in Foundry that is missing, as there are third parties such as AIM Security and PromptFeed that are not integrated in Foundry as of now.

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 also enhance integration with Azure policies and Azure functions where orchestration can be done.

I have been using Azure AI Foundry for just over six months.

I think the stability and reliability of Azure AI Foundry are good, as clients are mostly experimenting and doing small-scale testing and proofs of concept, and at this point, it is working well.

I have not come across customer service and technical support from Microsoft for Azure AI Foundry, but from my past experience, it has sometimes been hit-and-miss. I have not tested it for Foundry, but in the past, you sometimes get good service and sometimes it has been circles.

Neutral

The biggest return on investment for someone who uses Azure AI, from my point of view, includes the playground and guardrails, as these are the two biggest investments. The playground is where you can deploy the model and test, and guardrails serve as the protection mechanism.

The main differences between Azure AI Foundry and other tools are that Foundry is a catch-all, one bucket, while in AWS, there are Bedrock and SageMaker, which are more spread out and do a lot more than Foundry.

Azure AI Foundry's data visualization capabilities play a pretty basic role in optimizing my operations, as I would prefer to use a third-party tool such as Tableau that provides even more visuals.

I have not utilized Azure AI Foundry's predictive analytics feature.

Azure AI Foundry has improved decision-making in my company.

I would describe the experience of the deployment of Azure AI Foundry as not challenging, but I think it is still in a growing phase and not enterprise-grade in terms of doing a lot of things. However, I need to understand how intuitive it is to configure and set up.

My advice to other companies considering Azure AI Foundry is that it should be a foundation, and then you have to supplement it with something else. Start with the foundation, which is Azure AI Foundry, and then build on top of that, as Foundry is not the only solution. I would rate this review overall at 6.5 out of 10.

We are still quite early in our understanding of what Azure AI Foundry can do for us, and we have plenty of use cases where we think AI can help. I am in the legal sector in the UK. For example, I think there are scenarios with lots of legal processes which could be digitized, and you could leverage AI for that, specifically looking at agents and how they can do some of that heavy lifting. We are developing and considering developing AI agents, and we plan to incorporate Azure AI Foundry Agent Service.

Azure AI Foundry has affected our management of privacy, performance, and compliance primarily based on our location in the UK, where it is more focused on the region in terms of where that data is being processed and who has access to it, which is hopefully no one other than us.

Regarding security, compliance, or governance features with Azure AI Foundry, I am not the best person to ask on that. However, I know that the fact that there is lots of customization possible really helps us. For instance, using that regional example, I know that we can process data in select regions, which is a really good example of how we can limit our risk exposure based on our governance policies and our regulators.

One of the big things Azure AI Foundry could improve is continuously evolving the governance elements and how, while I know they exist, the more control we can have over different elements and observation of what different agents are doing, the better.

I have only been using Azure AI Foundry in my current role for about twelve months.

I would assess the stability and reliability of Azure AI Foundry as quite good since I have not experienced any downtime, crashes, or performance issues. We are very early in the process.

I am trying to speak on behalf of our IT colleagues regarding Microsoft Support, which they use all the time, and from my understanding, their support offering is quite good. I would rate it as a nine, with always room for improvement.

Positive

Prior to adopting Azure AI Foundry, we had other AI solutions, but I do not think they necessarily cross direct paths with Azure AI Foundry.

It is too early to say how Azure AI Foundry has helped reduce the time taken for AI app and agent development cycles in our company, unfortunately, but we hope that it will be significant.

I do not think there were many solutions we actually considered before selecting Azure AI Foundry because this was all part of our Microsoft infrastructure, so it was a straightforward decision from our perspective.

We are deploying AI applications in a cloud environment. A lot of this is obviously relevant to Azure AI Foundry, but we also look at other solutions that are SaaS-based products, which are obviously cloud products. In terms of Azure AI Foundry, we would be cloud.

I believe we are utilizing Azure Machine Learning as a firm, but I do not think there is much that we are using there at present.

I do not have any experience with machine learning that I can speak about.

I could not confidently say which AI services we are utilizing or realizing the most impact from on a business level, to be honest. Anything I would say on that might be misleading.

I think we are using Bing and RAG, but I am not one of the IT colleagues who would be able to tell you in terms of what that looks like and what impact they have had.

I plan to use it in the future.

I have very limited experience with it, and it is more conceptual rather than having actually put something out there in the wild. It is very much controlled, with only a couple of IT experts involved.

I do not think we are using Azure AI Foundry Models for assessing various types of AI models so far. We are aware it exists; we have done some very brief testing on it, but nothing significant.

I think for us, it is still this huge opportunity with Azure AI Foundry. We have not seen any direct value from it yet, but we have a huge bank of opportunities that we want to try to develop in Azure AI Foundry for, so I think that is as much as I can say on that.

My experience with pricing, setup costs, and licensing of Azure AI Foundry is quite limited, so I do not know much about it.

I have not been involved in the deployment of Azure AI Foundry as yet, but I hope to be involved at some stage.

I would rate this review experience a nine out of ten.

The main use cases are to house our agents, to keep our agents organized, and to check the lifecycle of our agents and if they're performing or have any errors.

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.

Some examples of how its features have benefited my organization include ease of access, being able to see what's functioning, what's not functioning, why it's not functioning, and when it stopped functioning, and to maintain visibility on day-to-day operations.

Azure AI Foundry can be improved by adding educational features.

I have been working in my current field for 20 years.

Regarding stability and reliability, I've had zero downtime with Azure AI Foundry, and it helps fix itself.

There have been no crashes or performance issues unless there was an outage, such as the DNS outage that happened.

Azure AI Foundry scales very well with the growing needs of my organization because of our maturity state.

At this time, we haven't expanded usage. It's just trial and seeing what we can do with it and seeing where it's applicable and where we can push it.

I haven't had to reach out to customer service or technical support for it. It's been self-taught and self-learned so far. I've had no bad experiences with Microsoft support; they always point us in the right direction.

It's difficult to give something an assessment when you didn't reach out, so I'll go with a neutral rating regarding customer service and technical support.

Neutral

Prior to adopting Azure AI Foundry, my organization was not using another solution to address any needs. Because of how limited my company is in dealing and sharing with their IPP, we wouldn't have been able to go anywhere else. However, because Microsoft is already approved in our entire stack, we can get away with it.

I would describe my experience with deploying Azure AI Foundry as having had no problems. It's all just a learning curve on how I can use it, how I can implement it, and what I can and can't do with it.

I wouldn't be able to answer about the return on investment in monetary terms. Based on time itself, there is a great return on investment of time.

I have not considered any other solutions before selecting Azure AI Foundry.

My advice to another organization that's considering using Azure AI Foundry is don't be scared and don't be intimidated. It's new, and there is a lack of knowledge, but it helps you build itself. Once you start using it, you'll learn, and it's a trainable model, so you're basically training it and yourself to help develop what you need. I would rate this product an eight out of ten.

My main use cases for Azure AI Foundry are agents for specific workflows within the business.

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 data visualization capabilities play a significant role in optimizing my operations as it will make it easier for people using the services we provide if they are more visual, making it quite important.

I assess the integration of Azure AI Foundry with existing cloud services as good, but it can be improved.

Even though I have only been utilizing Azure AI Foundry for the past four months, I think the understanding between Copilot Studio and Azure AI Foundry is still somewhat unclear regarding which one to use when and why, and how they complement each other is a journey we are currently undertaking.

I have been using Azure AI Foundry for the last four months.

I would assess the stability and reliability of Azure AI Foundry as strong, as I have not experienced any downtime, crashes, or performance issues. However, Microsoft has had some blackouts recently, so it is more of a general Microsoft concern, not a Azure AI Foundry concern.

Azure AI Foundry scales great with the growing needs of my organization.

I have not had to use customer service and technical support yet on Azure AI Foundry.

Before selecting Azure AI Foundry, I considered none because we are a very large Microsoft organization, so it is easier for us to stick with what we know.

Prior to adopting Azure AI Foundry, I was not using another solution to address similar needs, although we were looking at other AI solutions. However, because we stick with what we know, we chose Azure AI Foundry.

I would describe my experience with deploying Azure AI Foundry as quite nice.

Everything with Azure AI Foundry has worked well, and I have not faced any challenges.

I have not seen a return on investment yet, but I think we will.

My experience with pricing, setup cost, and licensing of Azure AI Foundry is that it seems pretty clear, and I can put guardrails around safety and other concerns, so I think that is pretty good so far.

What led me to consider the change to Azure AI Foundry was all of the following: performance, cost, support, and scalability.

What I appreciate most about Azure AI Foundry is all of it, which is why I am using it.

I have not seen benefits from the features of Azure AI Foundry yet because we have just started out on our journey, but I think they will significantly benefit us when we can get the use case right.

I have not utilized Azure AI Foundry's predictive analytics feature.

Azure AI Foundry has not improved decision-making in my organization yet, but I believe it will when we use it in production.

Since I have been utilizing Azure AI Foundry for the past four months, my advice to another organization considering it is to pursue it; it would be unwise not to try it. I rated this review an eight out of ten.

My main use cases for Azure AI Foundry are agentic workflows. I do completions, such as chat completions, and other agent building and maintaining models within Azure. I am utilizing Document Intelligence and Content Understanding services of Azure AI Foundry. I use Azure AI Search for RAG capabilities. I use it to build knowledge bases for my models to work on so they have context of what our business processes are. I am using Azure AI Foundry models for accessing various types of AI models. I primarily use the GPT models right now, but I'm evaluating other options, especially with the new capabilities that have been built and released this week.

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 security, compliance, and governance feature that has been most useful or impactful for me is probably governance over what models are deployed and where they're being called from. In security, I am managing and knowing what agents are out there running under our organization, and where and how they're being used.

The observability feature that has been most useful for me includes governance components built within Azure AI Foundry that help expose data and information about what we're doing within the platform.

The feature I appreciate the most about Azure AI Foundry is the centrally contained, managed aspect of being a one-stop shop for us to build out our AI ecosystem.

I think Microsoft has been improving these, especially with some of the announcements they had this week, but my biggest critique is some of the fragmentation of their different AI services they have, including AI Open, OpenAI, Azure OpenAI, and Azure Foundry. They feel very disjointed sometimes, so having a unified single experience for all of that, which I think is what they're trying to do here with Azure AI Foundry and some of the improvements that they've made, would be ideal.

I have been using Azure AI Foundry for a year and a half.

I assess the stability and reliability of Azure AI Foundry as very good. I haven't had downtime yet, but my use cases are limited, so I haven't had extensive always-on uses of it yet, and we also haven't seen any downtime.

The platform expands to all of our needs without us really having to do anything, so scalability is definitely there.

I did not consider other solutions before because we are a Microsoft shop, so we quickly narrowed in on Azure AI Foundry, which is our primary tool because it fits within the ecosystem of where we're running our software and where all of our corporate data is stored.

At this point, I'm still investigating use cases, and I do not have an active return on investment that I've seen yet, but once I deploy these use cases out across our business, I hope that will materialize.

I do not utilize Azure Machine Learning yet, but it's something that I'm actively investigating.

In deploying Azure AI Foundry, a lot of it is just learning AI development, which isn't necessarily something specific to Azure, and then consolidating on which service really fits our needs. That was where the challenges were, but otherwise, it's somewhat smooth.

I would rate this product an 8.