Deploy production-ready vector databases for RAG, semantic search, recommendation engines, and real-time AI applications with sub-millisecond latency and infinite scalability.
Pinecone is a fully managed vector database designed for AI applications. It enables real-time similarity search at scale, supports hybrid search (vector + keyword), metadata filtering, and seamless integration with LLMs for Retrieval-Augmented Generation (RAG).
Sub-millisecond latency for real-time AI applications.
No infrastructure to manage — focus on building AI.
Combine vector and keyword search for precision.
Auto-scaling with zero downtime.
From embedding generation to production deployment — a streamlined process.
1
Embed: Convert text, images, or data into high-dimensional vectors using models like OpenAI, Cohere, or Sentence Transformers.
2
Index: Upsert vectors with metadata into Pinecone namespaces for organized storage.
3
Query: Perform real-time similarity search with filters and hybrid scoring.
4
Integrate: Connect with LLMs for RAG, chatbots, or recommendation pipelines.
5
Scale & Optimize: Monitor performance, auto-scale, and refine embeddings.
Sub-millisecond query latency at any scale.
Combine vector and keyword search with metadata filters.
Scale to billions of vectors with zero downtime.
Ground LLMs with up-to-date, domain-specific knowledge.
Isolate data with namespaces for secure access.
SOC 2, GDPR, encryption at rest and in transit.
Pinecone powers AI applications with fast, accurate, and scalable vector search.
Find relevant content beyond keywords for documents, portals, and knowledge bases.
Combine LLMs with enterprise data to build assistants and chatbots.
Personalize e-commerce, media, and content experiences.
Enable image search and real-time outlier detection for business applications.