Pinecone
The AI Knowledge Platform.
The Pinecone Database, Inference, and Assistant make building high-performance vector search apps easy. Developer-friendly, fully managed, and easily scalable without infrastructure hassles.
Once you have vector embeddings, manage and search through them in Pinecone to power semantic search, recommenders, and other applications that rely on relevant information retrieval.
Ultra-low query latency, even with billions of items. Give users a great experience. Live index updates when you add, edit, or delete data. Your data is ready right away. Combine vector search with metadata filters for more relevant and faster results.
Launch, use, and scale your vector search service with our easy API, without worrying about infrastructure or algorithms. We'll keep it running smoothly and securely.
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Zilliz Cloud
Zilliz Cloud is a fully managed vector database based on the popular open-source Milvus. Zilliz Cloud helps to unlock high-performance similarity searches with no previous experience or extra effort needed for infrastructure management. It is ultra-fast and enables 10x faster vector retrieval, a feat unparalleled by any other vector database management system. Zilliz includes support for multiple vector search indexes, built-in filtering, and complete data encryption in transit, a requirement for enterprise-grade applications. Zilliz is a cost-effective way to build similarity search, recommender systems, and anomaly detection into applications to keep that competitive edge.
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Amazon S3 Vectors
Amazon S3 Vectors is the first cloud object store with native support for storing and querying vector embeddings at scale, delivering purpose-built, cost-optimized vector storage for semantic search, AI agents, retrieval-augmented generation, and similarity-search applications. It introduces a new “vector bucket” type in S3, where users can organize vectors into “vector indexes,” store high-dimensional embeddings (representing text, images, audio, or other unstructured data), and run similarity queries via dedicated APIs, all without provisioning infrastructure. Each vector may carry metadata (e.g., tags, timestamps, categories), enabling filtered queries by attributes. S3 Vectors offers massive scale; now generally available, it supports up to 2 billion vectors per index and up to 10,000 vector indexes per bucket, with elastic, durable storage and server-side encryption (SSE-S3 or optionally KMS).
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Azure AI Search
Deliver high-quality responses with a vector database built for advanced retrieval augmented generation (RAG) and modern search. Focus on exponential growth with an enterprise-ready vector database that comes with security, compliance, and responsible AI practices built in. Build better applications with sophisticated retrieval strategies backed by decades of research and customer validation. Quickly deploy your generative AI app with seamless platform and data integrations for data sources, AI models, and frameworks. Automatically upload data from a wide range of supported Azure and third-party sources. Streamline vector data processing with built-in extraction, chunking, enrichment, and vectorization, all in one flow. Support for multivector, hybrid, multilingual, and metadata filtering. Move beyond vector-only search with keyword match scoring, reranking, geospatial search, and autocomplete.
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