Vertex AI networking overview

Vertex AI supports enterprise networking options for accessing Vertex AI endpoints and services that help you:

  • Safely access your Vertex AI resources from an on-premises or multicloud environment.
  • Protect your Vertex AI artifacts from exfiltration.
  • Configure network traffic for your Vertex AI resources.

This page is intended for enterprise networking architects and administrators who are already familiar with Google Cloud networking concepts.

Private access options for Vertex AI

Vertex AI supports the following options for accessing Vertex AI endpoints and services privately, without an external IP address:

The following table shows the supported access methods for connecting from on-premises and multicloud environments to Vertex AI services. In this table, a checkmark indicates that an access method is supported. For more information about using an access method with a specific Vertex AI service, click the Learn more link.

Internet Private Service Connect for Google APIs Private Google Access Private services access Private Service Connect endpoints
Batch predictions
Custom training (control plane)
Custom training (data plane)
Learn more
Generative AI Studio
Vector Search (index creation)
Vector Search (index query)
Learn more
Online prediction
Private online prediction endpoints
Learn more
Vertex AI Feature Store
Model Registry
Vertex AI Pipelines

Securing your Vertex AI resources

To reduce the risk of data exfiltration for your Vertex AI resources, you can place them within a service perimeter using VPC Service Controls.

What's next