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Pinecone

AI Infrastructure

Vector database for fast and accurate AI-powered search

Founded 2019San Francisco, CA, USA51–200 employeesFreemium · Free plan
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About Pinecone

Pinecone is a managed vector database designed for AI applications that require similarity search over high-dimensional embeddings. It is widely used in retrieval-augmented generation (RAG) pipelines, semantic search, recommendation systems, and anomaly detection. Pinecone handles indexing, storage, and querying of vector embeddings at scale, eliminating the infrastructure complexity of building vector search from scratch.

Key Features

Managed vector index with sub-millisecond queriesServerless and pod-based deployment optionsHybrid search combining vector and keyword searchMetadata filtering on vector resultsNamespace partitioning for multi-tenancyIntegrations with LangChain, LlamaIndex, and OpenAI

Who It's For

ML engineersDevelopersEnterprises building AI appsStartups

Industries Served

TechnologyHealthcareFinanceE-commerceMedia

Platforms

Web API

Support

Documentation Community Discord Enterprise support

Founders

  • Edo Liberty

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Pinecone FAQs

What is a vector database?

A vector database stores numerical representations (embeddings) of data and enables similarity search — finding records that are semantically similar to a query. This is essential for AI features like semantic search, RAG, and recommendations.

Does Pinecone have a free plan?

Pinecone offers a free Starter plan with one serverless index and limited storage, suitable for prototyping and small-scale applications. Paid plans unlock more indexes, storage, and production-grade SLAs.

Tags

Vector DatabaseSemantic SearchRAGEmbeddingsAI Infrastructure

Category

AI Companies

AI Infrastructure

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Official Website

pinecone.io

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