Pinecone
The vector database for AI applications
Pinecone is a managed vector database. If that sentence means nothing to you: it's a special database that stores data in a way that lets AI find similar things really fast. It's the backbone of RAG systems, recommendation engines, and semantic search.
pinecone.ioUse Cases
Semantic search
Search that understands meaning, not just keywords. "How to make my app faster" returns results about performance optimization.
RAG systems
Storing your company's documents as vectors so an LLM can retrieve relevant info before answering questions.
Recommendations
"Users who liked X also liked Y" — but based on actual content similarity, not just purchase history.
Anomaly detection
Finding things that don't fit the pattern — useful for fraud detection, QA, and content moderation.
Key Features
- →Fully managed — no infrastructure to wrangle
- →Serverless tier — pay only for what you use
- →Real-time indexing — data is searchable immediately
- →Metadata filtering — combine vector search with traditional filters
- →Scales to billions of vectors
Why This Matters for PMs
If your team is building anything with semantic search or RAG (and they probably are), they'll need a vector database. Pinecone is the most popular managed option. The alternatives are Weaviate, Qdrant, or just using pgvector in Postgres. The PM question: do we need a dedicated vector DB, or can we add vector search to our existing database? For most teams starting out, pgvector is fine. Pinecone makes sense at scale.