Official Resources

Documentation Release Notes Python Client TypeScript Client Vercel Starter Vertex RAG n8n Integration

Key Features

  • Serverless Architecture: Fully-managed, serverless auto-scaling pods with zero-infrastructure management and Bring Your Own Cloud (BYOC) on AWS & GCP.
  • Hybrid Index Types: Dense semantic vectors, sparse lexical vectors via SPLADEv2, and hybrid fusion for comprehensive RAG and metadata search.
  • Namespaces & Multi-Tenancy: Logical partitions inside indexes with cross-namespace backup and migration support for multi-tenant applications.
  • Backup & Restore: Full snapshot/export via API, console, or Terraform provider with point-in-time restore capabilities.
  • Integrated AI Services: Built-in embeddings (OpenAI, Cohere, Gecko, E5), rerankers (SPLADE, cross-encoder), and Pinecone Assistant marketplace plugin.
  • MCP Agent Integration: Open-source MCP server enabling agents to list_indexes, upsert, query, and manage indexes directly.
  • Enterprise Security: Encryption at rest & in transit, SOC 2/ISO 27001/GDPR/HIPAA compliance, private endpoints, and audit logs.
  • Multi-Language SDKs: Python, Node.js, Java, .NET, Go – all updated to API v2025-04 with async clients, sparse support, and integrated inference.

Code Examples

BYOC + Backup Setup

python 
import pinecone

pinecone.init(api_key=os.getenv("PINECONE_API_KEY"))

# create index inside your own VPC
pinecone.create_index(
    name="byoc-index",
    dimension=768,
    metric="cosine",
    spec=pinecone.ServerlessSpec(
        cloud="aws",
        region="us-east-1",
        project_id="my-gcp-project"     # required when BYOC
    )
)

# snapshot & restore
pinecone.create_backup("byoc-index", id="backup-2025-07-19")
pinecone.restore_index("byoc-index", backup_id="backup-2025-07-19")

LangChain Integration

python 
from langchain.vectorstores import Pinecone
import pinecone, os

pinecone.init(api_key=os.getenv("PINECONE_KEY"))
vectorstore = Pinecone.from_existing_index(
    "semantic-index",
    embedding=OpenAIEmbeddings()
)
docs = vectorstore.similarity_search("What is Pinecone?", k=3)

Vertex AI RAG Engine Setup

text 
# Follow the official notebook to wire Pinecone as the vector store 
# inside Vertex AI RAG pipelines.
# Required parameters: 
# - index name
# - dimension (768 for Gecko)
# - distance metric (cosine)
# 
# Reference: https://cloud.google.com/vertex-ai/generative-ai/docs/rag-engine/use-pinecone

n8n Low-Code Integration

text 
# Use the Pinecone Vector Store node in n8n to insert, update, 
# or retrieve vectors without writing code.
# 
# Available operations:
# - Insert vectors
# - Update vectors  
# - Retrieve similar vectors
# - Delete vectors
# 
# Reference: https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.vectorstorepinecone/

Use Cases

  • RAG pipelines - LangChain, Vertex AI RAG Engine, CrewAI, LangGraph integrations
  • Recommendation engines - Hybrid dense + sparse + rerank for e-commerce & media
  • Conversational agents - Pinecone Assistant plugin feeds context to LLMs
  • Enterprise search - Secure namespaces, audit logs, private endpoints
  • Workflow automation - n8n, Zapier, Temporal pipelines trigger vector operations

Pros & Cons

Advantages

  • Zero-infrastructure serverless scaling with auto-scaling pods
  • Integrated embeddings, rerankers, and backup solutions
  • Broad SDK & framework support across multiple languages
  • SOC 2/HIPAA/GDPR compliant with enterprise security
  • BYOC for data-residency & compliance requirements

Disadvantages

  • Metadata field size cap (~5k characters per field)
  • Serverless cost can spike at very high QPS volumes
  • Hybrid tuning adds complexity to search configuration
  • Fully managed - Limited low-level performance tuning
  • Single index throughput ceiling requires sharding for scale

Future Outlook & Integrations

  • Serverless GA Expansion [H2 2025]: Serverless GA on AWS & GCP with price-optimized tiers
  • MCP Ecosystem Growth [H2 2025]: More plugins for CrewAI, LangGraph, Firebase Genkit integration
  • BYOC Enhancements [H2 2025]: Terraform modules, private-service-connect on GCP
  • Multimodal Support [H2 2025]: Image & audio vector support with CLIP-style embeddings
  • Cost Observability [H2 2025]: Usage dashboards and budget alerts for cost management