Vertex AI Search
Definition
Google's enterprise search service with semantic understanding, vector search, and RAG for intelligent search experiences across enterprise data.
Use Cases
- The Home Depot: Improve on-site product search so customers can find items using natural language queries and intent (e.g., "quiet ceiling fan for bedroom"), including better relevance and fewer zero-result searches. — Uses Google Cloud search and AI capabilities for retail search experiences; integrates product catalog data and behavioral signals to improve relevance and discovery, and can incorporate semantic understanding for query interpretation. (Better product discoverability and search relevance, which supports improved customer experience and can contribute to higher conversion from search-driven sessions.)
- Carrefour: Enhance digital commerce search and discovery across a large product catalog, helping shoppers find relevant products with more natural queries and improved relevance. — Adopts Google Cloud data and AI services in its digital platform; integrates catalog and customer interaction data to improve search and recommendations, enabling more intelligent retrieval experiences. (Improved shopping experience through better discovery and relevance, supporting digital engagement and operational efficiency in maintaining search quality at scale.)
- Mayo Clinic: Help users find trustworthy health information across a large library of articles and clinical content using natural-language questions. — Uses Google Cloud search/AI approaches to organize and retrieve content; applies semantic retrieval to surface relevant documents and reduce time spent navigating complex information structures. (Faster access to relevant information and improved user experience when searching large, authoritative content repositories.)
Provider Equivalents
- AWS: Amazon Kendra
- Azure: Azure AI Search
- GCP: Vertex AI Search
Frequently Asked Questions
- What’s the difference between Vertex AI Search and Vertex AI Agent Builder?
- Vertex AI Search focuses on retrieving the best documents or passages from your content (web pages, PDFs, knowledge bases, etc.) using semantic understanding and relevance ranking. Vertex AI Agent Builder is used to create conversational agents that can call tools, follow workflows, and use retrieval (often via Vertex AI Search) to answer questions. In practice, Vertex AI Search is often the retrieval layer, while Agent Builder is the conversational and orchestration layer.
- When should I use Vertex AI Search instead of building search on OpenSearch/Elasticsearch?
- Use Vertex AI Search when you want a managed, Google-quality enterprise search experience with built-in semantic retrieval and faster time-to-value, especially for RAG-style applications and site/app search. Consider OpenSearch/Elasticsearch when you need deep low-level control over indexing, custom analyzers, cluster operations, or you already run and tune search infrastructure and want maximum flexibility.
- How much does Vertex AI Search cost?
- Pricing depends on factors like the edition/features you use, the number of documents indexed, query volume, and any generative AI/RAG features (which may add model inference costs). You should estimate cost by mapping expected indexing size and monthly queries, then include any additional charges for data ingestion, storage, and LLM usage if you generate answers. For exact numbers, use the current Google Cloud pricing page and the Google Cloud Pricing Calculator for your region and workload.
Category: gcp-services
Difficulty: intermediate
Related Terms
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