You are viewing docs for Pinecone v1. Switch to docs Pinecone v2.
What is Pinecone
Pinecone provides similarity search as a service. You can index billions of items in real-time and search for the closest matches with millisecond latency.
Key benefits of Pinecone:
- Managed: Launch a service and make API calls — leave infrastructure to us.
- Scalable: Each service is persistent, consistent, sharded, and replicated.
- Centralized: Load vector embeddings in streams or batches from your models, data lakes, or feature stores.
- Fresh: New and updated items become searchable in milliseconds.
- Fast: Low latency even with billions of items.
- Accurate: Our fast algorithms are more accurate than open-source options.
Similarity search is a new method of searching through big data. Unlike traditional search methods, it indexes and searches vector representations of data to find items in close proximity to the query. More about similarity search.
Vector embeddings, or simply “vectors,” are sets of floating-point numbers that represent objects, such as images and documents. They are often generated by ML models trained to capture the semantic similarity of objects. Deep Learning models almost always use vectors. More about vector embeddings.
Want to start with working examples? View and run our example notebooks.