Pinecone serverless is the most popular vector database, used by engineering teams to solve two of the biggest challenges in deploying GenAI solutions - data security and hallucinations - by allowing them to store, search, and find the most relevant information from company data and send only that context to Large Language Models (LLMs) with every query.

This workflow is called Retrieval Augmented Generation (RAG), and with the Pinecone vector database, it aids in providing relevant, accurate, and fast responses from search or GenAI applications to end users.

Vector databases are purpose-built for storing and searching through vector embeddings, AI representations of data. This method of information retrieval (IR) is called vector search. Vector search is the new standard for finding the most relevant data for GenAI applications or any kind of search application.

Pinecone Vector Database Usage-based Billing: Charges are calculated by pod price multiplied by pod count. Invoices reflect total index runtime, rounded to 15-minute increments.

Learn more about pricing at pinecone.io/pricing