Pinecone
Pinecone is a managed vector database that gives n8n AI agents fast, scalable semantic search and long-term memory for RAG.
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Best use case with n8n
Production vector search for n8n AI agents — storing embeddings and retrieving the most relevant context for RAG at scale.
Pinecone is a managed vector database built for one job and doing it extremely well: fast semantic search at scale. In n8n, it's the long-term memory that makes AI agents accurate and grounded.
Where it fits in a RAG workflow
- Scrape/ingest content (e.g. with Firecrawl).
- Create embeddings with OpenAI or Anthropic.
- Store vectors in Pinecone with useful metadata.
- On each user question, query Pinecone for the most similar chunks.
- Feed those chunks to the LLM for an accurate, sourced answer.
Pinecone vs Supabase (pgvector)
- Pinecone — purpose-built, serverless, scales to millions of vectors with minimal tuning. Best when search performance and scale matter.
- Supabase —
pgvectorin Postgres. Best when you already use Postgres or want one database for everything on a budget.
Start simple
For a small knowledge base, Supabase pgvector is plenty. Reach for Pinecone when you're scaling to large datasets or need consistently fast retrieval in production.
Give your n8n agents scalable memory
Pinecone delivers fast vector search for production RAG.
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Key features
- Fully managed, serverless vector database
- Fast similarity search at scale
- Metadata filtering on queries
- Namespaces for multi-tenant data
- Works with n8n via HTTP node or AI nodes
- Integrates with OpenAI/Anthropic embeddings
Pros & cons
Pros
- Purpose-built for vector search — very fast
- Scales to millions of vectors effortlessly
- Managed — no infrastructure to run
- Free tier to prototype
Cons
- A dedicated service to manage and pay for
- Postgres + pgvector may suffice for small projects
- Costs grow with vector volume and queries
Alternatives
Supabase is an open-source Postgres backend with auth, storage, and vector search — a powerful database layer for n8n and AI workflows.
Pricing: Free tier (2 projects, 500MB DB); Pro ~$25/mo per project; usage-based above limits