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Hub resources overview

Note

You must use a hub based project for the features mentioned in this article. A Foundry project is not supported. For more information, see Project types.

Azure AI Hub is a resource type that is used in combination with Azure AI Foundry resource type, and is only required for selected use cases. Hub resources provides access to open-source model hosting and finetuning capabilities, as well as Azure Machine Learning capabilities, next to capabilities supported by its associated AI Foundry resource.

When you create an AI Hub, an Azure AI Foundry resource is automatically provisioned. Hub resources can be used in Azure AI Foundry and Azure Machine Learning studio.

Hubs have their own project types that support a differentiated feature set from Foundry projects. See project types for an overview of supported features.

Create an AI hub resource

Get started by creating your first hub in Azure AI Foundry portal, or use Azure portal or templates for advanced configuration options such as networking.

Hubs group one or more projects together with common settings including data access and security configurations. Projects act as folders to organize work and give access to developer APIs.

Create a hub-based project

To start developing, create a project. Hub-projects can be accessed in AI Foundry Portal to build with generative AI tools, and ML Studio to build with tools designed for custom machine learning model training.

Project concepts

Projects let you create and group reusable components that can be used across tools:

Asset Description
Data Dataset that can be used to create indexes, fine-tune models, and evaluate models.
Flows An executable instruction set that can implement the AI logic.​​
Evaluations Evaluations of a model or flow. You can run manual or metrics-based evaluations.
Indexes Vector search indexes generated from your data.

Projects also have specific settings that only hold for that project:

Asset Description
Project connections Connections to external resources like data storage providers that only you and other project members can use. They complement shared connections on the hub accessible to all projects.
Prompt flow runtime Prompt flow is a feature that can be used to generate, customize, or run a flow. To use prompt flow, you need to create a runtime on top of a compute instance.

Note

In Azure AI Foundry portal, you can also manage language and notification settings that apply to all projects that you can access regardless of the hub or project.

Share configurations across projects using hub

A hub shares configurations for a group of projects. As a team lead, consider creating a hub for use cases that share the same security configurations or business domain to avoid repetitive setup and let developers create their own project against the pre-configured environment.

Shared configurations managed on the hub include:

  • Security including public network access, customer-managed key encryption, and identity controls. Security settings configured on the hub automatically pass down to each project. A managed virtual network is shared between all projects that share the same hub.
  • Connections let you access objects in Azure AI Foundry portal that are managed outside of your hub. For example, uploaded data on an Azure storage account, or model deployments on an existing Azure OpenAI or AI Foundry resource. Optionally use connection to store shared credentials, so developers can implicitly access remote objects during development.
  • Compute and quota allocation is managed as shared capacity for all projects in Azure AI Foundry portal that share the same hub. This quota includes compute instance as managed cloud-based workstation for an individual. The same user can use a compute instance across projects.
  • Policy enforced in Azure on the hub scope applies to all projects managed under it.
  • Dependent Azure resources are set up once per hub and associated projects and used to store artifacts you generate while working in Azure AI Foundry portal such as logs or when uploading data. For more information, see dependent resources.

Access Azure AI Foundry models from hub-based projects

Hubs let you manage connections to existing Azure OpenAI or Azure AI Foundry resources, so you can use their models and selected customization capabilities in hub-based projects.

After a connection is created, model deployments are accessible via playground experiences. When you use Finetuning experiences in a hub-based project, your finetuning jobs are implicitly executed on the connected AI Foundry resource (default project context).

Storage and Key Vault dependent resources

Azure AI Hub is an implementation of Azure Machine Learning and requires multiple Azure services as a dependency.

Resource type Resource provider Kind
Azure AI Foundry hub Microsoft.MachineLearningServices/workspace hub
Azure AI Foundry project Microsoft.MachineLearningServices/workspace project
Azure AI Foundry or
Azure AI OpenAI Service		 Microsoft.CognitiveServices/account AIServices
OpenAI

If not provided by you, the following dependent resources are automatically created.

Dependent Azure resource Resource provider Optional Note
Azure AI Foundry Microsoft.CognitiveServices/accounts Provides access to models and other core Foundry APIs.
Azure Storage account Microsoft.Storage/storageAccounts Stores artifacts for your projects like flows and evaluations. For data isolation, storage containers are prefixed using the project GUID, and conditionally secured using Azure ABAC for the project identity.
Azure Key Vault Microsoft.KeyVault/vaults Stores secrets like connection strings for your resource connections. For data isolation, secrets can't be retrieved across projects via APIs.
Azure Container Registry Microsoft.ContainerRegistry/registries ✔ Stores docker images created when using custom runtime for prompt flow. For data isolation, docker images are prefixed using the project GUID.
Azure Application Insights &
Log Analytics Workspace		 Microsoft.Insights/components
Microsoft.OperationalInsights/workspaces		 ✔ Used as log storage when you opt in for application-level logging for your deployed prompt flows.
Azure AI Search Microsoft.Search/searchServices ✔ Provides search capabilities for your projects.

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