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Amazon Aurora Machine Learning
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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.

Amazon Aurora Serverless v2
Featured

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.

Instaclustr Vector Database Management

A managed service and tooling offering from Instaclustr that helps teams operate and optimize vector databases for GenAI and Retrieval-Augmented Generation (RAG) workloads, providing expertise and infrastructure management for production deployments.

Qdrant Cloud Inference

Qdrant Cloud Inference is a managed inference service integrated with the Qdrant vector database, allowing users to generate embeddings and work with vector search pipelines directly in the cloud environment.

Nextbrick Managed Vector Database Service

A fully managed vector database infrastructure and operations service provided by Nextbrick. It focuses on deployment, configuration, tuning, scaling, security, and maintenance of vector databases for AI and similarity search workloads. The service handles sharding, replication, query optimization, backups, and disaster recovery so organizations can offload operational management and focus on building AI applications.

MotherDuck

A cloud data warehouse that can be leveraged to store vector embeddings as List data types, enabling semantic search capabilities through SQL-based similarity functions within an existing data pipeline.

Snowflake

A cloud data platform that offers capabilities for storing and querying various data types, including vector embeddings, often used in conjunction with its data warehousing features.

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