An introduction to the Pinecone vector database.

Pinecone makes it easy to build high-performance vector search applications. It’s a managed, cloud-native vector database with a simple API and no infrastructure hassles.

Key benefits of Pinecone:

  • Fast: Ultra-low query latency at any scale, even with billions of items.
  • Fresh: Live index updates when you add, edit, or delete data.
  • Filtered: Combine vector search with metadata filters for more relevant, faster results.
  • Fully managed: Easy to start, use, and scale, while we keep things running smoothly and securely.

Key concepts

Vector search

Unlike traditional search methods that revolve around keywords, it is done by indexing and searching through ML-generated representations of data — vector embeddings — to find items most similar to the query.

Vector embeddings

Vector embeddings, or “vectors,” are sets of floating-point numbers that represent objects. They are generated by embedding models trained to capture the semantic similarity of objects in a given set.

You need to have vector embeddings to use Pinecone.

Vector database

A vector database indexes and stores vector embeddings for efficient management and fast retrieval. Unlike a standalone vector index, a vector database like Pinecone provides additional capabilities such as index management, data management, metadata storage and filtering, and horizontal scaling.

Example use cases

Want to see more and start with working example notebooks? See: Example Applications

  • Semantic text search: Convert text data into vector embeddings using an NLP transformer (eg, a sentence embedding model), then index and search through those vectors using Pinecone.
  • Question-answering: Index a set of questions as vectors and retrieve the most similar question’s answer for any new question.
  • Image similarity search: Transform image data into vector embeddings and build an index with Pinecone. Then convert query images into vectors and retrieve similar images.
  • Product recommendations: Generate product recommendations for ecommerce based on vectors representing users.

Overview of the workflow


Follow these guides to set up your index::

  1. Create an index
  2. Connect to an index
  3. Insert the data (and vectors) into the index

Once you have an index with data, follow these guides to start using your index:

Pricing and deployment options

Visit the pricing page for pricing and deployment options.

Get started with Pinecone

Go to the quickstart guide to get a production-ready vector search service up and running in minutes.