Technology RadarTechnology Radar

Pinecone

rag
This item was not updated in last three versions of the Radar. Should it have appeared in one of the more recent editions, there is a good chance it remains pertinent. However, if the item dates back further, its relevance may have diminished and our current evaluation could vary. Regrettably, our capacity to consistently revisit items from past Radar editions is limited.
Trial

Pinecone is the leading fully-managed vector database — designed for teams that want production-grade semantic search and RAG without operating any database infrastructure.

Buy vs Build

Pinecone is a pure buy: fully managed SaaS, no servers to run. You interact via API and pay per query and storage. If you want self-hosted, look at Qdrant or Weaviate instead.

Why It's in Trial

For teams without an existing PostgreSQL investment, or building at a scale where pgvector's limits become real, Pinecone offers:

  • Zero operations: No index tuning, no memory management, no capacity planning — Pinecone handles it
  • Serverless tier: Scale to zero; you pay only for what you query. Ideal for variable-traffic applications
  • Dedicated read nodes: For high-QPS production workloads where consistent sub-10ms latency matters
  • Pinecone Assistant: A fully managed RAG endpoint — upload documents, get grounded answers back with citations. Zero infrastructure, zero chunking code.
  • SOC 2 Type II + HIPAA: Compliance certifications important for regulated industries

Pinecone vs pgvector — The Decision

Factor pgvector Pinecone
Already on Postgres ✅ Natural fit ❌ Extra vendor
Scale >100M vectors ⚠️ Gets difficult ✅ Designed for it
Operations budget ✅ Minimal (managed Postgres) ✅ Zero (fully managed)
Compliance (HIPAA) ⚠️ Your responsibility ✅ Pinecone certified
Cost at 50M vectors ✅ Much cheaper ❌ More expensive
Hybrid BM25 search ❌ Not native ✅ Supported

Getting Started

from pinecone import Pinecone

pc = Pinecone(api_key="your-api-key")
index = pc.Index("my-index")

# Upsert vectors
index.upsert(vectors=[{"id": "doc1", "values": [0.1, 0.2, ...], "metadata": {"text": "..."}}])

# Query
results = index.query(vector=[0.1, 0.2, ...], top_k=5, include_metadata=True)

Key Characteristics

Property Value
Type Fully managed SaaS
Pricing Serverless (pay-per-use), Dedicated pods
Compliance SOC 2 Type II, HIPAA
Hybrid search Vector + BM25
Provider Pinecone Inc.
Website pinecone.io
Docs docs.pinecone.io

Further Reading