Vertex AI Vector Search
activeby Google Cloud · since 2021 · also known as Vertex AI Matching Engine, Agent Retrieval, Gemini Enterprise Agent Platform Vector Search
Google Cloud's managed ANN service built on ScaNN, now part of Gemini Enterprise Agent Platform.
Overview
Vertex AI Vector Search, originally Matching Engine, is Google Cloud's managed approximate nearest neighbour service built on Google Research's ScaNN algorithm, the same technology behind Google Search and YouTube retrieval. You build indexes from embeddings in Cloud Storage or stream updates, deploy them to endpoints backed by provisioned nodes, and query dense, sparse or hybrid vectors with namespace restricts and numeric filters. Google has folded Vertex AI into the Gemini Enterprise Agent Platform and renamed the newer collection-based Vector Search 2.0 to Agent Retrieval, with auto-generated embeddings and reranking. Best for GCP-native teams with large, high-QPS workloads; minimum node costs make it pricey for small ones.
Strengths & trade-offs
Strengths
- +ScaNN delivers excellent recall/latency at billion scale
- +Dense, sparse and hybrid queries
- +Streaming index updates for fresh data
- +IAM, VPC Service Controls and CMEK integration
- +Tight integration with Gemini embeddings and agent tooling
Trade-offs
- −Provisioned nodes mean a baseline cost even when idle
- −GCP-only
- −Two generations (1.0 indexes vs Agent Retrieval collections) and renames cause confusion
- −Metadata filtering is less expressive than in Qdrant or Weaviate
Great for
- +Large-scale semantic search on GCP
- +Recommendation candidate retrieval
- +RAG for Gemini-based agents
- +Image and product similarity
Think twice for
- −Small or spiky workloads on a budget
- −Multi-cloud or on-prem deployments
- −Full-text-heavy search
Capabilities
Native or official first-party support · 6 of 25 tracked
Scale & consistency
- Comfortable data size
- Massive · Multi-PB
- Horizontal scaling
- Native
- Multi-region
- No
- Transactions
- None
- Consistency
- Eventual
- Schema
- Schemaless
- Architecture
- Distributed
- Query languages
- Vector Search API (restricts and numeric filters)
Indexes shard across nodes and autoscale replicas; billions of vectors at high QPS are supported.
Deployment
available on · Google Cloud
Ecosystem
Alternatives to Vertex AI Vector Search
Fully managed serverless vector database with built-in embedding, reranking and assistants.
vertex-ai-vector-search vs pinecone →Google's Postgres-compatible service with disaggregated storage, a columnar engine and ScaNN vectors.
vertex-ai-vector-search vs alloydb →Microsoft's managed search service for full-text, vector, hybrid and agentic retrieval on Azure.
vertex-ai-vector-search vs azure-ai-search →Cloud-native, distributed open-source vector database built for billions of vectors.
vertex-ai-vector-search vs milvus →Fully managed Milvus from its creators, with serverless, dedicated and BYOC deployments.
vertex-ai-vector-search vs zilliz-cloud →Vector storage and similarity queries built into S3, trading latency for very low cost at scale.
vertex-ai-vector-search vs amazon-s3-vectors →Vertex AI Vector Search FAQ
What is Vertex AI Vector Search?
Vertex AI Vector Search is a vector database by Google Cloud, first released in 2021. Vertex AI Vector Search, originally Matching Engine, is Google Cloud's managed approximate nearest neighbour service built on Google Research's ScaNN algorithm, the same technology behind Google Search and YouTube retrieval. You build indexes from embeddings in Cloud Storage or stream updates, deploy them to endpoints backed by provisioned nodes, and query dense, sparse or hybrid vectors with namespace restricts and numeric filters. Google has folded Vertex AI into the Gemini Enterprise Agent Platform and renamed the newer collection-based Vector Search 2.0 to Agent Retrieval, with auto-generated embeddings and reranking. Best for GCP-native teams with large, high-QPS workloads; minimum node costs make it pricey for small ones.
Is Vertex AI Vector Search open source?
No. Vertex AI Vector Search is proprietary, closed-source software.
Does Vertex AI Vector Search support ACID transactions?
No. Vertex AI Vector Search does not provide ACID transactions; its consistency model is eventual.
Does Vertex AI Vector Search scale horizontally?
Vertex AI Vector Search scales horizontally out of the box, with built-in sharding or partitioning. It is comfortable at massive scale (Multi-PB).
How can I run Vertex AI Vector Search?
You can use the vendor's managed cloud. It is available on Google Cloud.
What is Vertex AI Vector Search best for?
Vertex AI Vector Search is a strong fit for large-scale semantic search on GCP, recommendation candidate retrieval and RAG for Gemini-based agents. Think twice for small or spiky workloads on a budget.
What are the best alternatives to Vertex AI Vector Search?
Common alternatives to Vertex AI Vector Search include Pinecone, AlloyDB for PostgreSQL, Azure AI Search, Milvus and Zilliz Cloud. The right choice depends on your workload, scale and hosting; WhichDB's smart match ranks them against your requirements.
How popular is Vertex AI Vector Search?
Vertex AI Vector Search ranks #141 of 844 databases on the WhichDB Score (21.2/100), and #11 among vector databases. The score combines public adoption signals such as package downloads, GitHub activity, job posts and developer discussion.