What it is
Pinecone is a cloud vector database: it stores embeddings (numeric representations of what a text or image means) and finds the closest matches in milliseconds. It's the piece AI chatbots and search tools rely on to answer from your own documents (RAG). It's fully managed and serverless, so there are no servers or indexes to tune, but it can't be self-hosted.
How to use it
- Create an index and upsert embeddings through the API or the Python and TypeScript SDKs.
- Generate embeddings and rerank results with built-in models, no extra provider needed.
- Combine semantic and keyword search with hybrid indexes.
- Keep each customer's data apart with namespaces.
- Build an assistant that answers from your documents with Pinecone Assistant.
What it's good for
- A law firm searching thousands of contracts by meaning, not just exact words.
- An online store recommending products similar to what a shopper is viewing.
- A SaaS support chatbot answering from its knowledge base while keeping each customer's data separate.
Pros
- Serverless: scales without picking server sizes or tuning indexes
- Built-in embedding and reranking models
- Namespaces to isolate each customer's data
- Ready-made connectors in n8n, Airbyte and major AI frameworks
Cons
- Not open source and can't be self-hosted
- The Standard plan has a monthly minimum even at low usage
- Console and docs are English-only
