Pinecone is a managed vector database for retrieval, recommendations and long-term memory in AI applications. Its current platform is built around the Documents API: you write JSON documents with your own field names, and the index schema decides how each searchable field is indexed — full-text, Pinecone-generated embeddings, or dense/sparse vectors you bring. Everything else is stored as filterable metadata.
The product line now includes Pinecone Database (serverless vector DB), Nexus (a knowledge layer for agents), Dedicated Read Nodes, Assistant, BYOC and the open-source pg_pinecone Postgres extension.
Core capabilities
Multiple search modes in one index — full-text (BM25 with Lucene syntax, stemming, stop words, n-grams), semantic, sparse-vector and hybrid search.
Integrated embedding — upsert and query with text; Pinecone generates vectors server-side, so you do not run a separate embedding pipeline.
Metadata filtering and reranking — narrow by metadata, then rerank for relevance.
Namespaces — keep each tenant's data separate inside one index for multi-tenant apps.
Serverless scaling — object-storage-based architecture that scales reads and writes without capacity planning.
Dedicated Read Nodes — provisioned read capacity for sustained high query volumes.
Backups and BYOC — back up indexes and deploy Pinecone in your own cloud account with zero-access networking.
MCP server — connect MCP-compatible agents to Pinecone for search and index management.
Assistant and Nexus — higher-level services for retrieval-augmented apps and agent knowledge.
What it is good at
Pinecone fits RAG, semantic search, recommendations, agent memory and multi-tenant retrieval where you want a managed service and predictable latency. The Documents API reduces the glue code around embeddings and lets one index serve keyword, vector and hybrid queries.
Getting started
Create a Pinecone account and an index; choose the schema fields you want searchable.
Write documents through the Documents API (text, vectors or both).
Query with text or vectors; add metadata filters and reranking as needed.
Use namespaces for tenant isolation.
For agents, connect the Pinecone MCP server or use an SDK integration.
For production, configure backups and consider BYOC or dedicated read nodes.
Pricing
Pinecone offers a free starter index and usage-based pricing after that, with Standard and Enterprise plans plus BYOC options. See pinecone.io/pricing for current rates.
What is Pinecone? Pinecone is a coding tool listed on Scriptiii AI Tools. Managed vector database service for search and retrieval in AI applications. Pricing: Freemium. Free tier with paid upgrades. Best for: ai-agents, tool, vector-databases.
Questions & answers
Everything you need to know about Pinecone
What is Pinecone?
Pinecone is a coding tool. Managed vector database service for search and retrieval in AI applications.
What does Pinecone cost?
Freemium. Free tier with paid upgrades.
What is Pinecone best for?
ai-agents, tool, vector-databases. It is filed under Coding.
