Pinecone
Managed vector database for production retrieval workloads
Pinecone is profiled here as a RAG Framework tool for engineering teams. Read about features, pricing, and how it compares to related options in the tools directory.
Description
Pinecone is a fully managed vector database founded in 2019 by Edo Liberty, a former research leader at Amazon and Yahoo. It indexes high-dimensional embeddings so teams can serve semantic search, recommendations, and RAG retrieval without operating index infrastructure themselves. A serverless architecture separates storage from compute, and recent releases added dedicated read nodes plus a bring-your-own-cloud deployment that keeps data inside the customer's account. The company reports 800,000+ active developers on the platform, and its 2026 releases extend the database toward agent-facing knowledge infrastructure.
Key Capabilities:
Serverless vector similarity search with metadata filtering
Namespaces for multi-tenant data isolation
Hybrid retrieval with sparse vectors and native full-text search
Integrated embedding and reranking inference
Dedicated read nodes for high-throughput, low-latency workloads
BYOC deployment on AWS, GCP, and Azure with SOC 2 and HIPAA options
Alternative tools
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- Voyage AI
Retrieval-optimized embedding and reranking models
- Chroma
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- Qdrant
Rust-based vector search engine with rich filtering