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.

Pinecone has the following attributes:

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

Get started using Pinecone.

Use cases

Pinecone is useful for a broad variety of applications. The following are some of the most common:

  • Semantic text search: Convert text data into vector embeddings using an NLP transformer such as a sentence embedding model, then index and search through those vectors using Pinecone.
  • Generative question-answering: Retrieve relevant contexts to queries from Pinecone and pass these to a generative model like OpenAI to generate an answer backed by real data sources.
  • Hybrid search: Perform semantic and keyword search over your data in one query and combine the results for more relevant results.
  • 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.

Want to see more and start with working example notebooks? See our example applications.

Key concepts

Vector search

Unlike traditional search methods that revolve around keywords, vector databases index and search through ML-generated representations of data, called vector embeddings, to find items most similar to the query.

Vector embeddings

Vector embeddings are sets of numbers that represent objects. They are generated by embedding models trained to capture the semantic similarity of objects in a given set. Pinecone supports two kinds of vector embeddings: dense embeddings and sparse embeddings.

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.

Learn more about vector databases.



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

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