Vector Databases for Embeddings with Pinecone

In roughly three hours you use Pinecone's managed vector database from Python — creating and working with indexes, then building search and RAG applications on top. It's practical and aimed at Python developers adding AI features, and not for you if you can't code or you specifically want a self-hosted, open-source option instead of a managed service.

The costs here come in layers worth understanding up front: the DataCamp subscription (about $14/month billed annually, first chapter free); Pinecone itself, which has a free starter tier but bills larger indexes and heavier use; and any embedding APIs, billed separately again by their provider. Managed services also change tiers and APIs often, so check Pinecone's current setup before building (as of 2026).

Vector Databases for Embeddings with Pinecone teaches the storage layer behind modern AI search and RAG. Over about three hours you use Pinecone's managed vector database from Python — working with pods and indexes, then building real applications like a semantic search engine and a RAG chatbot wired to OpenAI's API.