Vertex AI

What it is

Vertex AI brings model access, data pipelines, fine-tuning, vector search, prompt management, evaluation and deployment into one Google Cloud product. Its model garden includes Google's own models alongside third-party and open models. Agent and retrieval components let developers ground model output in their own indexed content.

Why it matters

Much of the AI-assisted search experience people encounter is built rather than bought, and Vertex AI is one of the main places that building happens. For search and growth teams, it is where site search, support assistants and content generation pipelines are assembled, so decisions made there shape how a brand's own information is retrieved and presented. Understanding it helps marketers brief engineering work realistically.

How it works

Practitioners index content into a vector store or use the managed search and RAG components, then call a model through the API with retrieved context and a system prompt. Evaluation tooling compares prompt or model variants against test sets, and deployed endpoints are monitored for latency, cost and quality. Access, data residency and logging are handled through standard Google Cloud controls.

When it applies

It applies when you are building custom AI features, internal tools or grounded search experiences on Google Cloud and need repeatable deployment, monitoring and cost control.

Examples

• An ecommerce team indexes product data in Vertex AI Search to power natural language product discovery on site.
• A publisher uses grounding with Google Search to reduce fabricated details in AI-assisted article summaries.
• A SaaS company fine-tunes a smaller model on support transcripts to classify incoming tickets before routing.

How it is measured

• Answer groundedness or citation accuracy on a held-out evaluation set
• Retrieval precision and recall for the indexed corpus
• Cost per thousand requests and token spend by application
• Endpoint latency and error rate in production