Pinecone

Pinecone is an AI developer tool that provides a fully managed vector database for building semantic search, RAG systems and agents that query company knowledge, made for engineering teams who do not want to run their own search infrastructure.

What is a vector database like Pinecone?

It stores numerical representations (embeddings) of text, images or other data and returns the items most similar to a query in milliseconds. In Pinecone, writes are acknowledged in under 100 ms and become searchable within seconds, indexing is automatic and speed holds steady as data grows. It supports dense, sparse and full-text indexes, plus inference services to create embeddings and rerank results.

What else does the platform include?

• Pinecone Assistant, which builds an assistant over your documents without wiring up a RAG pipeline.
• Nexus, a knowledge engine that compiles enterprise data for agents and serves it through a single query.
• A Claude Code plugin, a web console with metrics, and Prometheus and Datadog monitoring.
• Deployment on AWS, Google Cloud or Azure, and through cloud marketplaces.

Customers include Adobe, Microsoft, Zapier, Cisco and Mercado Libre.

Pinecone pros and cons

Pros: no servers to manage or algorithms to tune, plus SOC 2 Type II, HIPAA, GDPR and ISO 27001 compliance with SSO, RBAC and private networking. Cons: it is proprietary, so moving to another vector store means reindexing; metered usage can be hard to forecast for large workloads, and small projects may get by with pgvector inside Postgres.