LLM InfrastructureCommercial✦ Free Tier

Pinecone

Managed vector DB service

App Infrastructure

About

Fully managed vector database with serverless indexes. No ops overhead. The go-to managed option for production RAG.

Choose Pinecone when…

  • You want managed, scalable vector search
  • You don't want to run infra and need SLAs
  • High availability and zero ops for vector DB matter

Builder Slot

What knowledge does your AI have?Optional for most stacks

The memory layer — stores and retrieves vector embeddings for RAG and semantic search

Dev Tools
Not applicable
App Infra
Optional
Hybrid
Optional

Other tools in this slot:

Stack Genome Detection

AIchitect's Genome scanner detects Pinecone in your project via these signals:

npm packages
@pinecone-database/pinecone
pip packages
pineconepinecone-client
env vars
PINECONE_API_KEYPINECONE_ENVIRONMENT

Integrates with (3)

LangChainPipelines & RAG

LangChain wraps the Pinecone client in its vectorstore interface, making managed vector search available in any retrieval chain.

Production-scale semantic search inside LangChain — no infrastructure to manage, retrieval scales automatically with Pinecone.

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LlamaIndexPipelines & RAG

LlamaIndex stores and queries embeddings in Pinecone via its PineconeVectorStore adapter.

Managed, auto-scaling vector retrieval for LlamaIndex at production scale with no infrastructure to operate.

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HaystackPipelines & RAG

Haystack connects to Pinecone as a managed document store backend for its retrieval pipelines.

Scalable, managed vector retrieval inside Haystack with no infrastructure to operate.

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Alternatives to consider (6)

Pricing

✦ Free tier available
StandardUsage-based

Ruled out by 2 stacks

OSS Self-Hosted AI Stack
Managed cloud vector DB — data residency requires a self-hosted alternative
EU / GDPR Regulated AI Stack
US-based managed service without EU data residency guarantees

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Pinecone on AIchitect[![Pinecone](https://aichitect.dev/badge/tool/pinecone)](https://aichitect.dev/tool/pinecone)

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