Azure AI Foundry is a powerful platform for developing and scaling AI solutions. It gives teams structure through hubs and projects, shared resources, and collaborative tools. But to get the most from Foundry, it is important to plan carefully. From resource organization to cost management, a little forethought can make your AI journey smoother and more efficient.
Hub and Project Organization
Think of hubs as the control centers and projects as the workshops. To stay organized, you need a clear plan for both. Use hubs to centralize management of shared resources and users across related projects. Then, create projects for specific AI applications within each hub.
For example, a business might set up a Marketing hub, an HR hub, and a Finance hub. Each hub contains shared resources that are automatically available to all projects under it, keeping things simple and consistent.
Connected Resources
Connected resources are services made available at either the hub or project level. When you connect a resource at the hub level, every project in that hub can use it without developers needing direct access. This reduces complexity and improves security. If a project needs something unique, you can always add connected resources at the project level.
Security and Authorization
Managing access is critical. In Foundry, roles define what a user can and cannot do.
Hub-level roles include:
- Owner: Full access, including creating hubs and managing permissions.
- Contributor: Full access but cannot manage hub permissions.
- Azure AI Developer: Can do almost everything except create new hubs or manage permissions.
- Azure AI Inference Deployment Operator: Can create resource deployments.
- Reader: Read-only access, assigned automatically to all project members.
Project-level roles are similar but focused within a project. They include Owner, Contributor, Azure AI Developer, Azure AI Inference Deployment Operator, and Reader. Choosing the right role assignments keeps development efficient while ensuring proper oversight.
Regional Availability
Not all services are available everywhere. Before starting, check which regions support the features and models you need. This is especially important if your organization has compliance requirements or data residency rules.
Costs and Quotas
Costs in Foundry go beyond the AI services themselves. You also need to account for the resources that support hubs and projects, such as storage, compute, and networking. Quotas are another key factor. They limit usage and help control costs, but sometimes you may need to request an increase to handle heavy workloads or higher model usage.
Takeaway
Success in Azure AI Foundry is not only about technology, it is about planning. Organize hubs and projects thoughtfully, make smart use of connected resources, assign roles carefully, and always check regional availability, costs, and quotas. With these considerations in mind, your team can focus less on overhead and more on building impactful AI solutions.
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