Features
- Fully managed vector database as a service
- Real-time vector indexing and querying
- Metadata filtering alongside vector search
- Serverless and pod-based deployment options
Pros
- Zero operational overhead, fully managed
- Scales automatically with serverless option
- Low-latency queries even at billions of vectors
Cons
- Proprietary, vendor lock-in risk
- Pricing can be expensive at scale
- Less flexibility than self-hosted alternatives
Overview
Pinecone is a fully managed vector database designed for similarity search at scale. It handles the infrastructure complexity of vector indexing, storage, and querying, allowing developers to focus on building AI applications rather than managing database infrastructure.
Pinecone provides two deployment models: serverless (pay-per-query with automatic scaling) and pod-based (dedicated infrastructure with predictable performance). Both support real-time vector upserts, low-latency queries, and metadata filtering to combine vector similarity with traditional attribute-based filtering.
As one of the earliest purpose-built vector databases, Pinecone has a mature product with extensive integrations across the AI ecosystem, including LangChain, LlamaIndex, and the major AI SDKs.
When to Use
Choose Pinecone when you want a fully managed vector database with zero operational overhead. It is ideal for teams that want to build RAG applications without managing vector database infrastructure.
Getting Started
npm install @pinecone-database/pineconeimport { Pinecone } from '@pinecone-database/pinecone'
const pc = new Pinecone({ apiKey: process.env.PINECONE_API_KEY })
const index = pc.index('my-index')
await index.upsert([{ id: '1', values: [0.1, 0.2, 0.3] }])