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Google Vertex AI offers managed vector search capabilities as part of its AI platform, supporting hybrid and semantic search for text, image, and other embeddings.
Azure AI Search provides vector search capabilities as a managed service, supporting approximate KNN, hybrid search, and integration with other Azure AI tools.
AWS has introduced vector search in several of its managed database services, including OpenSearch, Bedrock, MemoryDB, Neptune, and Amazon Q, making it a comprehensive platform for vector search solutions.
MongoDB is a general-purpose database that now includes vector search capabilities, enabling light vector workloads alongside traditional database functionality. MongoDB Atlas, the managed cloud offering, includes vector search built on Lucene, supporting ANN queries and hybrid search. MongoDB Atlas Search integrates powerful vector search capabilities directly within MongoDB.
A vector search capability integrated within MongoDB Atlas, enabling vector-based retrieval and similarity search over unstructured data. Relevant for users seeking vector search in a popular database platform. MongoDB Vector Search is an integrated feature in MongoDB Atlas that enables efficient vector-based search within a comprehensive document database, supporting up to 2,048 dimensions and hybrid search capabilities.
Zilliz Cloud is a fully managed vector database service powered by Milvus, offering hassle-free deployment, scalability, and high performance for vector search applications.
Google Vertex AI is a fully-managed, unified AI development platform from Google Cloud for building, training, deploying, and managing machine learning and generative AI models at scale.
For full details and a pricing calculator, see Vertex AI Pricing.
managed-service, vector-search, hybrid-search, semantic-search, cloud-native