Storing the representation, retrieving the context
Pinecone stores and indexes embedding vectors alongside metadata and references to content. An application can embed its query, retrieve similar vectors and use the associated material as context. The embedding model, database and answer-generating model perform different jobs within that pipeline. [1]
A serverless architecture moved into general availability
Pinecone made its serverless database generally available in May 2024, after a public preview. The architecture separates reads from writes and storage from compute, using object storage for indexed vectors. Its cost and answer-quality comparisons were company claims, rather than guarantees for every retrieval workload. [2]
Sources
- What is a Vector Database & How Does it Work? Use Cases + Examples
Pinecone · retrieved Oct 23, 2026
- Pinecone Makes Accurate, Fast, Scalable Generative AI Accessible to Organizations Large and Small with Launch of its Serverless Vector Database
Pinecone · May 21, 2024