Metal

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.

Amazon Aurora Machine Learning provides managed vector storage and search capabilities integrated into Aurora PostgreSQL for AI workloads on AWS. Key features include serverless scaling, direct ML model calls via SQL for embeddings, and seamless integrations with Bedrock and SageMaker. Perfect for RAG pipelines and enterprise AI applications, it simplifies vectorization and abstracts infrastructure compared to self-hosted options like Milvus.

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.

The DataRobot vector databases feature provides FAISS-based internal vector databases and connections to external vector databases such as Pinecone, Elasticsearch, and Milvus. It supports creating and configuring vector databases, adding internal and external data sources, versioning internal and connected databases, and registering and deploying vector databases within the DataRobot AI platform to power retrieval-augmented generation and other AI use cases.

AlloyDB for PostgreSQL offers managed vector storage and search for AI workloads on Google Cloud, with optimized HNSW indexing. It supports serverless scaling, hybrid vector-relational queries, and integrations with Google Cloud ecosystem and pgvector. Suited for RAG pipelines and enterprise AI requiring ACID compliance, it provides superior performance and management ease versus self-hosted databases like Milvus.

An AWS document database service compatible with MongoDB, identified as a great choice for vector database needs.