

Find out in this report how the two AI Development Platforms solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
Each one we've carefully measured ROI and been able to demonstrate significant ROI with them.
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 playground is where you can deploy the model and test, and guardrails serve as the protection mechanism.
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.
On a scale from one to ten, I would rate customer service and technical support as a nine.
I am receiving full support due to our partnership with Microsoft and because we are in the evaluation phase.
It is important for organizations like Microsoft to apply OpenAI solutions within their own structures.
If the initial support personnel cannot resolve a query, it escalates to someone with more expertise.
Azure AI Foundry scales with the growing needs of my organization very well.
The platform expands to all of our needs without us really having to do anything, so scalability is definitely there.
I've had a couple of times where I've had to get to the VP level of Microsoft before I could get the capacity I needed for my customers.
The scalability depends on whether the application is multimodal or uses a single model.
The API works fine, allowing me to scale indefinitely.
I have not experienced any downtime, crashes, or performance issues.
Regarding stability and reliability, I've had zero downtime with Azure AI Foundry, and it helps fix itself.
I would assess the stability and reliability of Azure AI Foundry as very good.
Overall, it is acceptable, but the major issue we currently face in this project is the hallucination problem.
The solution works fine, particularly for enterprises or even some small enterprises.
Providing data on the internal workings of Azure AI Foundry would help customers like us feel more comfortable adopting it.
What did not work well for us regarding Azure AI Foundry includes the security piece, being able to identify how to deploy to multiple regions, reducing latency, and managing tokens per minute.
With code, you know what the binary result is, but with prompting, it is a lot harder.
They should consider bringing non-OpenAI models also into their fold, just as AWS Bedrock, which provides its own models and models from other commercial providers through the Bedrock service.
Expanding token limitations for scaling while ensuring concurrent user access is crucial.
Azure needs to work on its own model development and improve the integration of voice-to-text services.
Regarding the pricing, setup cost, and licensing of Azure AI Foundry, I would say it is fair, but I think it gets more expensive.
the pricing, setup costs, and licensing for Azure AI Foundry are very expensive, but still cheaper than hiring an additional position
It was difficult to get an understanding of how we could model out our pricing and cost over time without talking to someone.
The pricing is very good for handling various kinds of jobs.
Recent iterations have increased token allowances, mitigating some challenges associated with concurrent user access at scale.
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.
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.
The feature that has been the most beneficial for enhancing customer experience is the one that allows you to compare multiple models to one another and see how they perform against each other.
OpenAI models help me create predictive analysis products and chat applications, enabling me to automate tasks and reduce the workforce needed for repetitive work, thereby streamlining operations.
The most valuable features are Azure AI Foundry; we use Azure AI Foundry to deploy various Azure OpenAI agents within Azure, such as Assistant, Azure OpenAI Assistant using Azure AI Foundry.

| Company Size | Count |
|---|---|
| Small Business | 2 |
| Midsize Enterprise | 2 |
| Large Enterprise | 13 |
| Company Size | Count |
|---|---|
| Small Business | 16 |
| Midsize Enterprise | 1 |
| Large Enterprise | 19 |
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?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.
Azure OpenAI integrates advanced language models with robust security for precise information extraction and task automation. Its seamless Azure integration and drag-and-drop interface simplify implementation and enhance accessibility.
Azure OpenAI offers a comprehensive suite of features designed for efficient data processing and task automation. It provides high precision in extracting information and strong conversational capabilities, crucial for developing chatbots and customer support systems. Its integration with Azure ensures seamless data handling and security, addressing key enterprise requirements. Users can employ its versatile GPT models for diverse applications such as predictive analytics, summarizing large documents, and competitive benchmarking. Despite its strengths, it faces challenges like latency, inadequate regional support, and limited integration of new technologies. Improvements in model fine-tuning and more flexible configuration are desired by users.
What features make Azure OpenAI a reliable choice?Azure OpenAI is implemented across industries like healthcare, finance, and education for tasks like invoice processing, digitalizing records, and language translation. It enhances policy management, document assimilation, and customer support with predictive analytics and keyword extraction. Organizations in such sectors benefit from streamlined workflows and task automation.
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