

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.
The product offers a significant return on investment through its scalability and integration capabilities.
My customers have seen returns on investment through increased efficiency, automated calculations, improved accuracy in pricing, and reduced staffing needs due to the automation.
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.
The support quality depends on the SLA or the contract terms.
The community access is weak, which limits the ability to engage in discussions and find documentation and examples of similar cases effectively.
I would rate the technical support of IBM Watson Studio a solid ten out of ten.
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.
Watson Studio is very scalable.
I have had a chance to communicate with the technical support of IBM Watson Studio, which has been responsive and helpful.
I rate IBM Watson Studio seven out of ten for scalability because while it scales, it requires significant resources to do so, making it expensive compared to some competitors.
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.
Expertise in optimization is necessary to manage such issues effectively.
I find IBM Watson Studio to be quite robust, with minimal downtime and great support regarding stability and reliability.
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.
The platform is associated with a complicated setup process and demands heavy hardware, making it expensive to scale.
One area that could be improved is the backup and restoration of the database and the overall database configuration.
I wish learning IBM Watson Studio could be easier and more gradual, as it is a complex task.
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.
IBM Watson Studio is considered rather expensive, with a rating of six or seven.
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.
This capability saves a significant amount of time by automating processes that typically involve manual work, such as data cleaning, feature engineering, and predictive analytics.
I believe the AutoAI features of IBM Watson Studio have significantly helped in my data projects by automating model selection and hyperparameter tuning.
It integrates well with other platforms and offers good scalability.

| Company Size | Count |
|---|---|
| Small Business | 2 |
| Midsize Enterprise | 2 |
| Large Enterprise | 13 |
| Company Size | Count |
|---|---|
| Small Business | 12 |
| Midsize Enterprise | 1 |
| Large Enterprise | 5 |
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.
IBM Watson Studio provides tools for data scientists, application developers and subject matter experts to collaboratively and easily work with data to build and train models at scale. It gives you the flexibility to build models where your data resides and deploy anywhere in a hybrid environment so you can operationalize data science faster.
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