Pinecone is a vector database built for accurate, secure, and scalable AI applications that rely on embeddings and similarity search.
Vector search for AI apps
Pinecone provides cloud infrastructure to store and query large volumes of vector data with low latency, making it suitable for production search and retrieval workflows.
Key capabilities include:
- Fast similarity search with low query latency
- Metadata-based filtering
- Real-time data updates
- API access for integration with multiple programming languages
- Support for different embedding (vector) models
Pinecone is designed to integrate with common machine learning frameworks and major cloud platforms.
How it works
Pinecone runs as a cloud service available via a web interface and API. A typical setup looks like:
- Sign up on the Pinecone website
- Create an index (choose vector dimension and similarity metric)
- Convert your data into vectors using an embedding model
- Upload vectors and metadata to the index
- Run queries by embedding the request and retrieving the most similar items
Pricing includes a limited free tier and paid plans starting at $0.096 per hour. The service interface is in English, and some Pinecone components are open source for customization.
Notable features
- Scales to billions of vectors
- Access controls and user management
- Cloud integrations: AWS, Azure, and GCP
