Introduction

Google is embedding Gemini across its cloud and productivity stack. From Vertex AI to BigQuery ML and Workspace APIs, you can add models, embeddings, and agents without leaving GCP. We give a concise map and when to use which integration.

Vertex AI and Model Garden

Models and endpoints Vertex hosts Gemini and other models with unified APIs, fine-tuning, and MLOps. We summarize model selection, deployment, and how to keep cost and latency under control.

Agents and RAG Vertex Agent Builder and related tools let you assemble RAG and agent flows with minimal code. We outline when that’s enough vs. when to build custom pipelines.

Data and Analytics

BigQuery and Gemini BigQuery ML and Gemini integration let you run inference and natural language over your data warehouse. We cover use cases (e.g. semantic search, summarization) and governance.

Workspace and Duet For internal tools and productivity, Gemini in Workspace and Duet APIs offer another surface. We note how they fit with Vertex and data residency requirements.