Production LLM Observability Stack
Full-stack observability architecture tracking telemetry across the four pillars of enterprise GenAI: Operational Metrics, Cost, Quality, and Security.
SYSTEM TOPOLOGY & DATA FLOW
ENTERPRISE SPECIFICATION
The Core Problem Solved
Traditional APM tools (Datadog/NewRelic) monitor CPU, memory, and HTTP 500s, but fail to detect hallucinations, prompt drift, token bloat, or gradual accuracy decay.
When To Deploy This Architecture
All production LLM applications where reliability, customer trust, and financial predictability are vital.
Architectural Components
- OpenTelemetry Instrumentation for GenAI
- Trace Collector & Session Replay
- Asynchronous Eval Worker (Sampling production traces for LLM-as-a-judge checks)
- Drift & Anomaly Detector (Embedding cluster shift tracking)
- Cost & Quota Attribution Ledger
Data Flow Narrative
Application Request -> OpenTelemetry Span Capture -> Async Kafka / SQS Queue -> Observability Backend (Langfuse / OpenLIT) -> Async Eval Engine -> Alerting Webhooks.
Security & Perimeter Control
Automatic client-side hashing and redaction of PII before trace serialization into telemetry stores.
Governance & Telemetry
Provides compliance records demonstrating SLA compliance, uptime, and factual accuracy metrics over time.
Identified Failure Modes & Mitigations
Telemetry queue backpressure during traffic spikes; sampling bias hiding rare catastrophic hallucination failures.