What is Vector Search?
Vector Search is Google Cloud’s managed service for vector similarity search, now part of the Gemini Enterprise Agent Platform (formerly Vertex AI). The service was originally called Matching Engine and, more recently, Vertex AI Vector Search. It is built on Google Research’s ScaNN algorithm (Scalable Nearest Neighbors), the same retrieval technology that powers Google Search, YouTube, and Google Play. It performs approximate nearest neighbor (ANN) search: instead of comparing every vector exactly, it finds the most similar vectors approximately, which keeps latency low even across billions of entries.
The problem Vector Search solves: modern AI applications convert text, images, and other data into embeddings, numerical vectors whose proximity expresses semantic similarity. Classic relational search is not suited to this. Vector Search indexes these embeddings and answers similarity queries in real time. This makes it the retrieval layer for retrieval-augmented generation (e.g. via RAG Engine), semantic search, and recommendation systems.
Core Features
- ANN search with ScaNN: Approximate nearest neighbor search based on the ScaNN algorithm delivers relevant matches across very large datasets at low latency. Recall measurement lets you balance accuracy against speed.
- Hybrid search and filtering: Vector Search combines dense embeddings (semantic) with sparse embeddings (keyword-based). Boolean filters (restricts) on numeric and text attributes limit searches to subsets of the data.
- Flexible connectivity: Index endpoints can be deployed through public endpoints or privately via VPC peering and Private Service Connect. Streaming updates refresh the index in near real time.
- Integration with the AI ecosystem: The service works with Vertex AI embeddings and frameworks such as LangChain and LlamaIndex, and supports multimodal search across text and image embeddings.
Typical Use Cases
Retrieval-augmented generation (RAG): For a user question, an application retrieves the most relevant document chunks from the vector index and passes them as context to an LLM. This grounds responses in current, proprietary data and reduces hallucinations.
Semantic search across enterprise data: A company searches large document or product collections by meaning rather than exact keywords. Users find relevant content even when their wording differs from the stored description.
Recommendation systems: A platform suggests items similar to a given product or piece of content by comparing their embeddings in vector space. This technique powers recommendations at large providers such as eBay and Mercado Libre.
Benefits
- Proven retrieval technology: ScaNN is the same foundation used by Google Search, YouTube, and Google Play
- Scales to very large embedding datasets at low latency for real-time applications
- EU regions, private endpoints (PSC/VPC), and CMEK support for data sovereignty requirements
Integration with innFactory
As a certified Google Cloud Partner, innFactory supports you with the adoption and operation of this service.
Typical Use Cases
Frequently Asked Questions
What is Vector Search (formerly Vertex AI Vector Search)?
Vector Search is Google Cloud's managed vector similarity search service, formerly known as Matching Engine and, more recently, Vertex AI Vector Search, now part of the Gemini Enterprise Agent Platform. It is built on Google Research's ScaNN algorithm and performs approximate nearest neighbor (ANN) search across very large embedding datasets. Applications index embeddings and query for the nearest vectors at low latency.
When should I use Vector Search?
Use the service when you need to find the most similar vectors quickly across large volumes of embeddings. Typical scenarios are retrieval-augmented generation to ground LLM responses, semantic search across document collections, and recommendation systems for products and content. Multimodal search across text and image is also supported.
How much does Vector Search cost?
Billing is infrastructure-based. You pay per node-hour for the compute nodes or replicas that host the deployed index, plus costs for building and updating the index. Costs scale with dataset size, the number of replicas, and query volume. The official Vertex AI pricing page lists binding prices.
Is Vector Search available in the EU and how is it connected?
Yes. The service is available in EU regions, including europe-west1 (Belgium) and europe-west4 (Netherlands). Index endpoints can be deployed publicly or privately through VPC peering and Private Service Connect (PSC). Customer-managed encryption keys (CMEK) are supported, which helps when data sovereignty requirements apply.
Note: All product information on this page has been compiled with care, but is provided without guarantee and may be outdated or incomplete. Cloud services evolve rapidly — features, pricing, SLAs, and availability change frequently. Authoritative and up-to-date information can only be found on the official product page of Google Cloud (official documentation). This page does not represent an offer by Google Cloud.
