Yes. Gemini 3 integrates cleanly with Google Cloud through Vertex AI and Gemini Enterprise. This means you can use Gemini 3 as a fully managed service with built-in authentication, VPC-SC support, IAM roles, and audit logging. Google Cloud treats Gemini 3 much like other managed ML endpoints, so enterprise teams can deploy it without changing their existing security and governance patterns. You can call it from Cloud Run, GKE, Cloud Functions, App Engine, or backend microservices using standard service accounts.
Because Gemini 3 is available directly in Vertex AI, it fits into existing Cloud storage and data infrastructure. You can store documents in Cloud Storage, reference them in prompts, and combine Gemini 3 with BigQuery for data analysis workflows. Enterprises that use centralized policy enforcement, org policies, or private networking can apply the same patterns to Gemini 3 endpoints. Gemini Enterprise also adds collaboration features, model evaluation tooling, and data governance layers, making it easy for large organizations to manage AI usage.
For retrieval-based applications, Gemini 3 can sit on top of Google Cloud while connecting to vector databases inside or outside GCP. A common architecture is: store embeddings inMilvus or Zilliz Cloud., store raw documents in Cloud Storage or BigQuery, and let a Cloud Run or GKE service orchestrate retrieval plus Gemini 3 calls. This creates a clean, modular pipeline that fits enterprise requirements for security, monitoring, and scale while giving developers maximum flexibility.