Vertex Ai Guide

---
name: vertex-ai-guide
description: Build AI on Google Cloud with Vertex AI — Gemini models, Model Garden, Vector Search, and managed agents.
category: ai-research
---

## Overview

Vertex AI is Google Cloud's unified ML platform, and its generative AI stack
covers: Gemini models via a managed API, Model Garden (a catalog of Google,
open, and partner models deployable with one click), Vector Search for
large-scale retrieval, and managed agent tooling — all inside your GCP project
with IAM, VPC, and regional controls. For GCP-centric organizations, it's the
natural home for GenAI work, inheriting Google's model quality and cloud
enterprise controls together.

The distinctive assets: Gemini models (strong multimodal reasoning) available
with Google-scale serving, Model Garden's breadth (including open models you
can deploy without leaving GCP), and Vector Search for billion-scale ANN
retrieval backing RAG. The platform story is "everything in one GCP project."

Evaluate Vertex as GCP infrastructure with Google's models — the integration
with your existing cloud posture is the point, the models are the draw.

## When to use

- GCP-centric organizations building GenAI (inherit IAM, VPC, compliance,
  billing).
- Applications needing Gemini's multimodal capabilities (text+image+video+audio
  reasoning).
- Managed RAG at scale with Vector Search (billion-scale vector retrieval).
- Deploying open models from Model Garden inside your GCP boundary.
- Managed agents and orchestration within GCP.
- Google-cloud procurement and enterprise agreements.

## Core concepts

- **Gemini on Vertex**: managed Gemini models with GCP controls — regional
  endpoints, IAM, VPC-SC, data residency. Check model availability per region
  for your compliance needs.
- **Model Garden**: catalog of first-party, open, and partner models —
  deployable to endpoints with one click. The fastest path to "that open model,
  inside our GCP."
- **Vector Search**: managed large-scale vector search (formerly Matching
  Engine) for RAG retrieval at billion-vector scale. The retrieval backbone
  for serious GCP RAG.
- **Managed agents**: Vertex's agent tooling (Agent Builder, orchestration)
  for building assistants over your data and APIs.
- **Grounding and RAG**: managed grounding options including Google Search
  grounding and your own data via Vector Search. Choose the grounding source
  per use case.
- **IAM, VPC-SC, and regions**: enterprise controls — service perimeters,
  private endpoints, regional pinning. Use them; they're the reason to be on
  Vertex.
- **Provisioned throughput**: reserved capacity for production SLOs. Size from
  load tests, not guesses.
- **Model monitoring and evals**: Vertex's evaluation and monitoring tooling
  for tracking quality in production.

## Practical workflow

1. **Confirm regional availability.** Gemini models and features vary by
   region; compliance may constrain you. Verify before architecting.
2. **Set up IAM and perimeters.** Least-privilege access, VPC Service Controls
   where data sensitivity demands, private endpoints for model APIs.
3. **Benchmark Gemini and Garden models.** Same eval set across candidate
   models — include Gemini variants and relevant open models from the Garden.
4. **Build retrieval on Vector Search.** For RAG: index your corpus, tune
   chunking/embeddings, evaluate retrieval quality before building generation
   on top.
5. **Choose grounding deliberately.** Google Search grounding for
   fresh/public knowledge; your corpus via Vector Search for proprietary
   knowledge. Don't mix them accidentally.
6. **Deploy agents where the data is.** If tools and data live in GCP, Vertex
   agents minimize integration friction.
7. **Evaluate and monitor continuously.** Run evals on model/prompt changes;
   monitor quality, latency, and cost in production.

Checklist for Vertex production:
- Regional availability confirmed for required models/features.
- IAM least-privilege; VPC-SC where needed.
- Retrieval quality evaluated on Vector Search.
- Grounding sources chosen deliberately per use case.
- Provisioned throughput sized from load tests if SLOs require.

## Common pitfalls

- **Regional availability assumptions.** Building on features unavailable in
  your required regions. Verify first.
- **Vector Search as magic.** Billion-scale ANN doesn't fix bad chunking or
  embeddings. Evaluate retrieval quality on your corpus.
- **Grounding source confusion.** Mixing Google Search grounding with private
  corpus retrieval without deciding which answers which questions. Choose per
  use case.
- **Over-permissioned IAM.** Broad AI Platform roles. Scope per application.
- **On-demand for strict SLOs.** Shared capacity variability vs. production
  guarantees — provision throughput where SLOs demand it.
- **Model Garden sprawl.** Deploying many Garden models without evaluation
  discipline. Each deployment needs its own quality bar and cost model.
- **Ignoring data residency.** Assuming all Vertex processing stays in-region.
  Verify processing locations for your compliance requirements.
- **Cost without attribution.** Vertex spend across models, search, and agents
  without per-application tagging. Tag everything; review regularly.