Pinecone

A managed vector database that handles the infrastructure so you don't have to. You get sub-100ms p95 latency, auto-scaling to billions of vectors, and hybrid search with dense and sparse vectors out of the box. The serverless option is genuinely convenient for production RAG apps where you want predictable performance without tuning pods or managing clusters. Integrates cleanly with LangChain and LlamaIndex. Free tier gives you 100K vectors to prototype with, which is enough to know if it fits your use case. The tradeoff is cost and vendor lock-in compared to self-hosting Chroma or Weaviate, but that's the deal with any managed service. Metadata filtering works well for multi-tenant scenarios using namespaces.

skills.sh installs: 1K
GitHub stars: 14.4K
last updated: Aug 21, 2026

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Install

$ npx -y skills add orchestra-research/ai-research-skills --skill pinecone --agent claude-code

Installs into .claude/skills of the current project.

Files

Pinecone - Managed Vector Database

The vector database for production AI applications.

When to use Pinecone

Use when:

Need managed, serverless vector database
Production RAG applications
Auto-scaling required
Low latency critical (<100ms)
Don't want to manage infrastructure
Need hybrid search (dense + sparse vectors)

Metrics:

Fully managed SaaS
Auto-scales to billions of vectors
p95 latency <100ms
99.9% uptime SLA

Use alternatives instead:

Chroma: Self-hosted, open-source
FAISS: Offline, pure similarity search
Weaviate: Self-hosted with more features

Quick start

Installation

pip install pinecone-client

Basic usage

from pinecone import Pinecone, ServerlessSpec

# Initialize
pc = Pinecone(api_key="your-api-key")

# Create index
pc.create_index(
    name="my-index",
    dimension=1536,  # Must match embedding dimension
    metric="cosine",  # or "euclidean", "dotproduct"
    spec=ServerlessSpec(cloud="aws", region="us-east-1")
)

# Connect to index
index = pc.Index("my-index")

SKILL.md on GitHub