Pinecone Monitoring & Vector Query Tracing
Auto-instrument every Pinecone index operation, query, upsert, fetch, delete, with zero code changes. Pinecone spans attach as child spans inside your LangChain traces for complete RAG pipeline visibility.
Free tier · No credit card · 2-minute setup
What you get with Fluiq for Pinecone
Query latency tracing
Every index.query() call is a traced span with the number of results, filter metadata, namespace, and end-to-end latency including network time.
RAG pipeline child spans
Pinecone spans automatically attach as children of LangChain traces, cost and latency roll up to the parent pipeline without any extra configuration.
Upsert & fetch tracing
Index upsert, fetch, and delete operations are traced with vector counts and timing, monitor your data pipeline performance alongside query performance.
Add Fluiq to your Pinecone app in 2 lines
import fluiq
fluiq.instrument(api_key="fl_...") # patches Pinecone client automatically
from pinecone import Pinecone
import numpy as np
pc = Pinecone(api_key="your-pinecone-key")
index = pc.Index("my-index")
# All operations traced with latency and result counts:
index.upsert(vectors=[("id1", np.random.rand(1536).tolist(), {"source": "doc"})])
results = index.query(
vector=np.random.rand(1536).tolist(),
top_k=5, include_metadata=True
)What Fluiq instruments in Pinecone
Every call to these methods is automatically traced, no decorators, no wrappers, no manual spans.
Index.query()Index.query_async()Index.upsert()Index.upsert_async()Index.fetch()Index.delete()Index.describe_index_stats()Index.list()Start tracing Pinecone in 2 minutes
Free tier. No credit card. Full traces, security scanning, and evals on your first Pinecone call.
50,000 free traces / month · 1,000 evals / month · 14-day retention