Do AI agents pick Pinecone?
How often agents choose Pinecone when a developer needs vector database — measured across Claude, GPT, Gemini.
95% CI 26%–39%
When developers ask an AI agent for vector db, Pinecone is picked 32% of the time — ranking #1 of 5 measured across Claude, GPT, Gemini.
0 docs in FineWeb · 274 URLs in Common Crawl
Level 1 · Basic Web Presence · 6/16 agent standards
2.5M installs / month ▼ 3% MoM
Get the full report
The per-model and per-surface breakdown for Pinecone — where it wins, where it loses, and to whom — plus an alert when the Pick Rate moves.
About Pinecone
A managed vector database for storing and searching high-dimensional embeddings in production AI applications.
Pinecone lets developers store, query, and manage high-dimensional vectors with metadata filtering, making it suited for use cases like semantic search, recommendation systems, and retrieval-augmented generation (RAG). It offers both serverless indexes that scale automatically and pod-based indexes for dedicated resources, along with built-in embedding and reranking models so you can either bring your own vectors or let Pinecone handle embedding generation.
Where to start
Sign up for an account to get an API key from the Pinecone console, then use the TypeScript SDK (server-side only) to create an index, upsert vectors, and run your first query.
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