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An on-demand, auto-scaling configuration for Amazon Aurora DB instances that automatically adjusts compute and memory capacity based on load, integrated with Knowledge Bases for Amazon Bedrock to simplify vectorization and database capacity management.
An on-demand serverless configuration for OpenSearch Service that simplifies the operational complexities of managing OpenSearch domains, integrated with Knowledge Bases for Amazon Bedrock to support generative AI applications.
A feature of Amazon Aurora that enables making calls to ML models like Amazon Bedrock or Amazon SageMaker through SQL functions, allowing direct generation of embeddings within the database and abstracting the vectorization process.
Microsoft Azure's managed service for PostgreSQL, which supports the pgvector extension, enabling robust vector database capabilities in the cloud for AI and machine learning workloads.
Built into the Salesforce platform, Data Cloud Vector Database ingests various large datasets from customer interactions, classifies and organizes unstructured data, and merges it with structured data to enrich customer profiles and store as metadata in Data Cloud. It enhances generative AI by providing more relevant, accurate, and up-to-date responses through improved data retrieval and semantic search capabilities.
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.
AstraDB (also known as Astra DB by DataStax) is a cloud-native vector database built on Apache Cassandra, supporting real-time AI applications with scalable vector search. It is designed for large-scale deployments and features a user-friendly Data API, robust vector capabilities, and automation for AI-powered applications.
Amazon Aurora Serverless v2 is an on-demand, auto-scaling configuration for Amazon Aurora DB instances. It automatically adjusts compute and memory capacity based on load, starting up, shutting down, and scaling capacity up or down as needed. This eliminates the need for manual database instance management, allowing users to run their database in the cloud without managing any database instances. It can be used alongside provisioned instances in existing or new database clusters.
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