Vertex AI Matching Engine is Google Cloud’s managed approximate nearest neighbor (ANN) service for high-scale vector search. It stores embeddings, builds specialized ANN indexes, and serves similarity queries with low latency from Google’s infrastructure. You use it when you want a fully managed vector search layer inside the same control plane as your models—especially if you prefer to avoid operating your own vector database and are comfortable with the service’s indexing options and limits. Typical use cases include semantic search, recommendations, and retrieval-augmented generation.
Operationally, you create an index (specifying distance metric and index type), upload vector files, and deploy an index endpoint. Queries provide a vector and optional filters, and Matching Engine returns nearest neighbors with IDs and scores. It’s designed for large corpora and online latency targets, and it integrates with Vertex endpoints, IAM, logging, and monitoring. This co-location can reduce operational overhead: the same deployment, security, and quota model applies across training, serving, and retrieval.
Deciding between Matching Engine and Milvus depends on control and portability needs. If you need tight, cloud-native convenience and are fine with service boundaries, Matching Engine fits. If you want deeper configurability, flexible indexing strategies (e.g., specific IVF/HNSW/PQ parameter choices), or portability across environments, Milvus is a strong option. In both cases, the pattern is similar: generate embeddings with a Vertex model, store vectors (Matching Engine or Milvus), query top-k with filters, and feed results into a generator or ranker. Evaluate on accuracy/latency/SLA, and pick the store that aligns with your operational and cost priorities.