Azure AI Foundry Reviews

My main use case for Azure AI Foundry is deploying custom AI agents.

What I like the most about Azure AI Foundry is customizing the agent and deploying a custom agent that is only for our company, not a shared AI.

The feature of being able to pick the right models has been the most beneficial for enhancing our customer service because some AI models are more expensive but slower, while others are faster and cheaper, allowing us to pick the right model for the right task that we are trying to solve.

Having Azure AI Foundry as a tool has benefited our organization significantly because our team has five data scientists, and this type of tool makes everything much faster. In the past, if we wanted to do machine learning and classification on our projects, we had to spend three months, but with the generative AI integration, we can finish things in a week.

Azure AI has improved decision making in our organization considerably.

I would appreciate it if Microsoft could improve Azure AI Foundry by releasing new features immediately because sometimes we have to wait for weeks or months to use them. Since these technologies are changing rapidly, once we decide on the platform and choose Microsoft, everything goes with Microsoft, which makes it difficult to move things around.

I have been using Azure AI Foundry for two to three years.

I have experienced some downtime, crashes, and performance issues.

I evaluate customer service and technical support positively because we have the enterprise license, which allows us to prioritize serious issues, ensuring that Microsoft support responds quickly.

I would rate my customer service and technical support experience as around eight. I give it an eight because sometimes the first person I talk to might not know everything, leading to an escalation to the second or third level, which results in some process overhead on the time we need to spend.

Prior to adopting Azure AI Foundry, we used AWS to address similar needs.

I would describe my experience with deploying Azure AI Foundry as smooth, as I don't have any major issues. The deployment model for Azure AI Foundry is mostly on the cloud.

We don't really focus on the financial aspect, as our company is a non-profit, but we mostly focus on engagement, working with the government and the industry. I cannot say we've seen a significant return on investment.

I would say the pricing, setup costs, and licensing for Azure AI Foundry are very expensive, but still cheaper than hiring an additional position because the AI reduces many workloads that we have to do, enabling us to use Azure AI Foundry to solve some of the tasks instead of hiring another person.

Before selecting Azure AI Foundry, we considered solutions from AWS, Elasticsearch, and other companies, but we ended up using Azure AI Foundry because it functions as a comprehensive platform, not just one software, providing all the tools that we need.

Azure AI Foundry scales effectively with our growing needs, serving our requirements successfully. We have expanded usage of Azure AI Foundry considerably. The process of expansion has been smooth, mostly just involving increased payment. I assess the stability and reliability of Azure AI Foundry as ten out of ten. My advice to another organization considering Azure AI Foundry is that Microsoft offers many AI models available that are very easy to use compared to any other cloud services. I would rate this product eight out of ten overall.

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.

My main use case is customers' implementation.

I like the security part of Azure AI Foundry the most.

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

I assess the integration of Azure AI Foundry with existing cloud services as easy.

The data visualization capabilities of Azure AI Foundry play a significant role in optimizing our operations.

It helped us grow our AI capacity for our customers and for inside the procedure.

Some examples of how these features have benefited our organization include automation and better comprehension of our procedures.

The technical team has utilized Azure AI Foundry's predictive analytics feature.

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

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

I have been using Azure AI Foundry for eight years.

I love the stability and reliability of Azure AI Foundry.

I have not experienced any downtime, crashes, or performance issues.

Azure AI Foundry scales really well with our growing needs, especially for the AI part, as it increased the procedure management for us.

I would say our customer service and technical support is a good eight.

I give it an eight because it is not a perfect product; it is a new one, but we still love it and think it can be really useful for us and our customers. It is going to be a good product.

I did not consider any other solutions before selecting Azure AI Foundry.

The technical team says deploying Azure AI Foundry is really easier than any other solution that they tried.

The team faced challenges as any solution would present, but they overcame them.

The deployment model is hybrid for the most part.

I have seen a return on investment for the customers for the most part.

I measure the improvement through our KPIs that we run into our PSAs and all the ConnectWise tools that we were using.

I would need to ask my technical team about my experience with the pricing, setup costs, and licensing.

I would go with the big ones, so maybe with Amazon, but still I am a Microsoft person, so I would not change.

It could be price-wise that led me to change to Microsoft.

I cannot share any data points or examples; that would need to be my technical team.

The advice I would give to another organization considering using Azure AI Foundry is that the faster they get to it, the better they are going to have ROI on the product. I would rate my overall experience with Azure AI Foundry as an eight.

My main use case involves building multi-agent workflows.

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.

I assess the integration of Azure AI Foundry with existing cloud services as quite effective. For example, we use Databricks for landing the data, so having communication established between the agent in Azure AI Foundry and a data agent in Databricks is valuable.

The Azure AI Foundry data visualization capabilities play a significant role in optimizing my operations by enhancing the Foundry UI experience for building multi-agent systems and orchestrating all of that, which would be beneficial going forward.

The most beneficial feature for enhancing the customer experience has not been utilized yet, as we have not deployed anything to production for our customer use. We are still in the proof-of-concept phase.

To improve Azure AI Foundry, I think the next release should mainly include self-hosted containers for AI agents and the ability to build multi-agent orchestration.

I have been using Azure AI Foundry for about six months.

I assess the stability and reliability of Azure AI Foundry as satisfactory. I have not experienced any downtime, crashes, or performance issues since our workload is not in production yet.

I am yet to evaluate how well Azure AI Foundry scales with the growing needs of my organization. In 2026 and beyond, we will be deploying a significant amount of agentic workload there, at which point we will have a better understanding.

On a scale from one being the worst and ten being the best, I would rate customer service and technical support as high. I am receiving full support due to our partnership with Microsoft and because we are in the evaluation phase.

Positive

Prior to adopting Azure AI Foundry, I was not using another solution to address similar needs.

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. The experience can improve based on the announcements they made at Ignite.

I did not face any challenges with deploying, as we handled everything. If we were to host our own agents, I did not see any challenges.

I will know if we have seen a return on investment with Azure AI Foundry after we launch our first use case to customers, which is projected for 2026.

In terms of pricing, I am still trying to understand and compare with other offerings to determine what is the most economical or the best value for the investment. At this point, I do not know if the pricing is competitive.

Before selecting Azure AI Foundry, I considered our existing partnership with Microsoft, which led us to choose Azure AI Foundry as our first option.

Azure AI Foundry has not improved the decision-making in my organization yet, as we have not evaluated that.

I cannot provide examples of how these features have benefited my organization, as we are still evaluating and in the proof-of-concept phase, waiting for announcements at Ignite. These announcements include self-hosted containers for AI agents and natively available MCB server, which would make multi-agent orchestration and invoking tools and APIs easier.

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

Once we deploy our production workload, if the scalability, speed, and performance are impressive, I will be sharing my experience with other customers.

I am providing this review with an overall rating of eight out of ten.