Question 1

What's the difference between Vertex AI Search and Vertex AI Vector Search?

Accepted Answer

Vertex AI Search is an end-to-end search application layer: it helps you ingest content, build a search experience, and return relevant results (often used for semantic search and RAG-style retrieval). Vertex AI Vector Search is a lower-level capability focused on storing and querying vector embeddings (nearest-neighbor search). In practice, Vertex AI Search may use vector retrieval under the hood, while Vector Search is what you use when you want to build custom retrieval and ranking pipelines yourself.

Question 2

When should I use Vertex AI Search?

Accepted Answer

Use it when you need Google-quality semantic search across documents, web pages, knowledge articles, product catalogs, or media metadata—and you want a managed service that reduces the work of building ingestion, indexing, relevance tuning, and retrieval for RAG. It’s a good fit for site search, customer support portals, internal knowledge search, and content libraries where keyword search alone misses intent.

Question 3

How much does Vertex AI Search cost?

Accepted Answer

Pricing depends on factors like the amount of content indexed, query volume, and enabled features (for example, semantic/vector retrieval and any generative/RAG-related capabilities). Costs typically include indexing/storage and per-query usage, plus any separate charges for embedding generation or LLM inference if you add generative answers. For accurate numbers, use the current Google Cloud pricing page and estimate based on documents/GB indexed and expected queries per month.

Vertex AI Search

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