# Cloud Native > Browse 24 items tagged with Cloud Native. ## Items - [Amazon Aurora Serverless v2](https://vectordb.ever.works/items/amazon-aurora-serverless-v2) — Amazon Aurora Serverless v2 is a cloud-hosted, serverless relational database (Postgres/MySQL compatible) with pgvector support for managed vector workloads, featuring auto-scaling compute/memory, pay-per-use pricing, automated backups, and multi-AZ/multi-region high availability. Suited for enterprise RAG via Amazon Bedrock Knowledge Bases and production AI apps. Provides easier operations than self-hosted Milvus or Postgres, deeply integrated with AWS unlike standalone Zilliz. - [Azure Database for PostgreSQL](https://vectordb.ever.works/items/azure-database-for-postgresql) — 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. - [Data Cloud Vector Database](https://vectordb.ever.works/items/data-cloud-vector-database) — 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. - [vector engine for OpenSearch Serverless](https://vectordb.ever.works/items/vector-engine-for-opensearch-serverless) — 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. - [Amazon RDS for PostgreSQL](https://vectordb.ever.works/items/amazon-rds-for-postgresql) — Managed PostgreSQL service from AWS with pgvector extension for vector embeddings and similarity search. Features include storage auto-scaling, read replicas, Multi-AZ high availability, and Row Level Security (RLS) enabling secure multi-tenant AI applications. Combines full SQL power and ACID transactions with vector capabilities, superior to dedicated vector DBs for complex relational queries and joins. - [Endee](https://vectordb.ever.works/items/endee) — High-performance vector database designed to handle up to 1B vectors on a single node with optimized indexing and execution. Also available as a managed cloud service. - [Aurora PostgreSQL-Compatible](https://vectordb.ever.works/items/aurora-postgresql-compatible) — An AWS database service compatible with PostgreSQL, identified as a great choice for vector database needs. - [Azure Cosmos DB](https://vectordb.ever.works/items/azure-cosmos-db) — A vector database solution provided by Microsoft Azure. - [Baidu VectorDB](https://vectordb.ever.works/items/baidu-vectordb) — Enterprise-level distributed vector database from Baidu Intelligent Cloud, built on the proprietary Mochow kernel, supporting up to 10 billion vectors with millions of QPS and millisecond latency. - [DashVector](https://vectordb.ever.works/items/dashvector) — Fully-managed, cloud-native vector search service from Alibaba Cloud based on the Proxima vector engine, offering horizontal scalability and instant vector updates for large-scale AI applications. - [KDB.AI](https://vectordb.ever.works/items/kdbai) — Cloud-native vector database platform for AI applications with high-performance similarity search. - [Tencent Cloud VectorDB](https://vectordb.ever.works/items/tencent-cloud-vectordb) — Fully managed, enterprise-level distributed vector database from Tencent Cloud, supporting billion-scale vector search with millisecond latency and millions of QPS using the self-developed Olama engine. - [Manu — A Cloud Native Vector Database Management System](https://vectordb.ever.works/items/manu-a-cloud-native-vector-database-management-system) — VLDB 2022 paper introducing Manu, a cloud-native vector database management system designed for scalable similarity search in cloud environments with separated storage and compute architecture. - [Vexless — Serverless Vector Data Management](https://vectordb.ever.works/items/vexless-serverless-vector-data-management) — SIGMOD 2024 paper introducing Vexless, a serverless vector data management system built on cloud functions that decouples compute and storage for elastic, pay-per-use vector similarity search. - [d-HNSW](https://vectordb.ever.works/items/d-hnsw-disaggregated-memory) — An efficient vector search system designed for disaggregated memory architectures. d-HNSW optimizes HNSW for environments where compute and memory are separated, typical in modern cloud and distributed systems. - [Quickwit](https://vectordb.ever.works/items/quickwit) — Cloud-native search engine for observability built on Tantivy, offering sub-second search on data stored in object storage as an open-source alternative to Datadog, Elasticsearch, Loki, and Tempo. - [Azure Cosmos DB Vector Indexing](https://vectordb.ever.works/items/azure-cosmos-db-vector-indexing) — Native vector indexing capability in Azure Cosmos DB that supports flat, quantizedFlat, and diskANN index types for efficient vector similarity search using the VectorDistance function. It enables low-latency, high-throughput, and cost-efficient vector search directly in Cosmos DB collections, with options for brute-force exact search (flat), compressed brute-force search (quantizedFlat), and approximate nearest neighbor search (diskANN). - [Amazon Web Services Vector Search](https://vectordb.ever.works/items/amazon-web-services-vector-search) — 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. - [Google Cloud Vertex AI Vector Search](https://vectordb.ever.works/items/google-cloud-vertex-ai-vector-search) — Google Cloud Platform offers vector search as part of its Vertex AI suite, enabling scalable and integrated vector search capabilities for AI-driven applications. - [Google Vertex AI](https://vectordb.ever.works/items/google-vertex-ai) — 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. - [Manu](https://vectordb.ever.works/items/manu) — A cloud-native vector database management system designed for efficient storage and retrieval of vector embeddings. Directly relevant as a vector database platform. - [Microsoft Azure AI Search](https://vectordb.ever.works/items/microsoft-azure-ai-search) — Azure AI Search provides vector search capabilities as a managed service, supporting approximate KNN, hybrid search, and integration with other Azure AI tools. - [Microsoft Azure Vector Database](https://vectordb.ever.works/items/microsoft-azure-vector-database) — Microsoft Azure offers vector search support across multiple database services, enabling developers to leverage vector search in cloud-native and enterprise scenarios. - [Transwarp Hippo](https://vectordb.ever.works/items/transwarp-hippo) — Transwarp Hippo is an enterprise-grade, cloud-native distributed vector database designed for scalable vector operations, including similarity search and clustering, targeting massive datasets and real-time recommendation systems. --- _Canonical page: https://vectordb.ever.works/tags/cloud-native_