Building a RAG pipeline from scratch means solving chunking, embeddings, hybrid ranking, and grounding separately. Vertex AI Search packages that complexity as a managed service.
Vertex AI Search (heir to what Google called Enterprise Search and Gen App Builder) is a managed retrieval-augmented generation service: it indexes a company's own documents -- PDFs, HTML, structured data in BigQuery, health FHIR records -- and exposes both a semantic search API and a generated-answer-with-citations API, without the team having to operate a vector database, a chunking pipeline, or a re-ranker separately.
The difference versus manually building RAG with Vertex AI Vector Search plus Gemini is the abstraction level: Vertex AI Search decides chunking, embedding, indexing, and hybrid (semantic + lexical) ranking internally. You sacrifice some fine-grained control in exchange for implementation time measured in days, not months.
Everything in Vertex AI Search revolves around the data store concept: a collection of documents with a defined type (unstructured, structured, website, or health/FHIR). A data store can be fed from Cloud Storage, BigQuery, or via prebuilt connectors to Jira, Confluence, SharePoint, and Salesforce, among others.
Vertex AI Search combines semantic search (via embeddings) with traditional lexical search (BM25-like) in a single ranking, which improves results in domains with technical jargon or exact product codes that pure embedding search tends to dilute. The "answer" API generates a natural-language response over the retrieved documents, including explicit references to the source fragments, letting a user verify the answer against the original document with one click.
A very common pattern is using a Vertex AI Search data store as a grounding source for an agent built in Agent Builder or a direct Gemini call: instead of the model answering from memory, the query first retrieves relevant passages from the data store and injects them as verified context before generating the final response. This measurably reduces hallucinations on questions about internal policies, product catalogs, or proprietary technical documentation.
When the organization has multiple data stores (one for product documentation, another for resolved support tickets, another for contracts), Vertex AI Search lets you configure a search app that queries several data stores simultaneously and blends the results into a single ranking, useful for internal assistants that need to answer questions spanning domains.
If the use case requires full control over chunking strategy (for example, chunks that respect complex financial table structures), a custom re-ranking algorithm, or a vector index shared across multiple products with sub-10ms latency requirements, building directly on Vertex AI Vector Search gives more room for tuning. Vertex AI Search performs better when the priority is implementation speed and minimal maintenance over granular pipeline control.
Vertex AI Search charges by volume of indexed documents and by number of queries, with an additional component if you use the included Gemini-powered answer generation. For large catalogs (hundreds of thousands of documents) with low query traffic, it's worth modeling indexing cost separately from query cost before committing, since both scale independently.
Carlos Montiel is an enterprise AI solutions architect. He implements LLMs, Agents, RAG, and orchestrators for companies across Guatemala and Latin America. Reach out for a consultation.
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