Pinecone Integration
Monitor vector upsert throughput, query latency, and index fullness across your Pinecone indexes. Get predictive capacity alerts and AI root cause analysis for your AI application vector search stack.
How It Works
Connect via Pinecone API Key
Add your Pinecone API key to TigerOps. The integration auto-discovers all your indexes, namespaces, and serverless vs. pod-based index configurations without requiring any infrastructure changes.
Configure Index & Namespace Monitoring
Select which Pinecone indexes and namespaces to monitor. TigerOps polls the Pinecone describe_index_stats API for vector counts, index fullness, and namespace-level distribution across your indexes.
Set Latency & Fullness Alerts
Define p99 query latency SLOs per index and index fullness warning thresholds. TigerOps fires alerts when query latency degrades or your pod-based index approaches capacity before you hit upsert limits.
Instrument Application-Level Metrics
Install the TigerOps Pinecone SDK wrapper to capture client-observed query latency, upsert batch sizes, and retry rates from your application — giving you ground-truth performance beyond what the API reports.
What You Get Out of the Box
Vector Upsert Throughput
Per-index upsert request rates, batch sizes, vectors upserted per second, and failed upsert rates. Track ingestion velocity and detect when your vector population pipeline falls behind.
Query Latency Monitoring
Client-side and server-side query latency at p50, p95, and p99 per index and namespace. Correlate latency changes with index fullness growth, topK size changes, or metadata filter complexity.
Index Fullness Tracking
Real-time index fullness percentage for pod-based indexes with trend analysis and capacity breach predictions. Alert before your index hits 100% fullness and upserts begin failing.
Namespace Vector Distribution
Per-namespace vector counts, namespace creation and deletion events, and namespace-level query throughput to understand multi-tenant index utilization and data distribution.
Serverless Index Cost Monitoring
Read unit and write unit consumption for Pinecone serverless indexes with daily and monthly trend analysis. TigerOps alerts on RU/WU cost anomalies and attributes spikes to specific operations.
AI Latency Root Cause Analysis
When Pinecone query latency spikes, TigerOps AI correlates the event with index fullness growth, metadata filter cardinality changes, upstream embedding model latency, and application retry storms.
TigerOps Pinecone Integration Setup
Connect TigerOps to Pinecone via the API and optionally instrument the Python client for client-observed latency.
# TigerOps Pinecone Integration Configuration
# API key from: https://app.pinecone.io/keys
integrations:
pinecone:
api_key_env: PINECONE_API_KEY
indexes:
- name: "product-embeddings"
environment: "us-east-1-aws"
type: serverless
alerts:
query_latency_p99_ms: 150
daily_read_units_warning: 5000000
- name: "user-embeddings"
environment: "us-east-1-aws"
type: pod
pod_type: p2.x1
alerts:
query_latency_p99_ms: 100
index_fullness_warning_pct: 80
index_fullness_critical_pct: 90
# Poll interval for index stats
poll_interval: 60s
# Python SDK instrumentation
# pip install tigerops-pinecone pinecone-client
# from tigerops.pinecone import instrument_index
# index = instrument_index(
# pc.Index("product-embeddings"),
# api_key=os.environ["TIGEROPS_API_KEY"],
# index_name="product-embeddings"
# )
exporters:
tigerops:
endpoint: "https://ingest.atatus.net/api/v1/write"
bearer_token: "${TIGEROPS_API_KEY}"Common Questions
Does TigerOps support both Pinecone serverless and pod-based indexes?
Yes. TigerOps monitors both index types with appropriate metric sets. Serverless indexes get read/write unit cost tracking. Pod-based indexes get fullness percentage, pod utilization, and replica health monitoring alongside the shared query latency and upsert throughput metrics.
How does TigerOps measure client-observed Pinecone query latency?
The TigerOps Pinecone SDK wrapper intercepts calls to the Pinecone Python or Node.js client and records end-to-end latency from the client perspective including network round-trip time. This gives you ground-truth latency independent of Pinecone service-side reporting.
Can TigerOps alert me before my Pinecone index reaches capacity?
Yes. TigerOps uses linear regression on index fullness growth to predict when a pod-based index will reach 100% capacity. You receive an early warning alert with the estimated time to breach and a recommendation to scale pods or archive old vectors.
How does TigerOps track Pinecone read unit consumption for cost control?
TigerOps polls the Pinecone describe_index_stats API on a configurable interval and computes RU consumption from query counts and vector dimensions. You can set daily and monthly RU budget alerts to catch cost anomalies before they impact your bill.
Does TigerOps support monitoring multiple Pinecone projects?
Yes. You can add multiple Pinecone API keys (for different projects or organizations) to a single TigerOps workspace. Each project gets its own dashboard and alert policies, with a unified cross-project view for total cost and latency SLO compliance.
Stop Discovering Pinecone Index Capacity Issues at 100% Fullness
Predictive capacity alerts, RU cost monitoring, and AI latency analysis. Connect in 5 minutes.