Monitoring and Alerting for Prompt Pipelines
Dashboards, anomaly detection, and on-call alerts for production prompts.
Production Prompt Pipelines Need Monitoring
A prompt pipeline in production is infrastructure — it needs dashboards, alerts, and runbooks just like any other service. Without monitoring, cost spikes, quality regressions, and latency blowups go unnoticed until users complain or bills arrive.
Core Metrics: Latency Percentiles
Track latency at P50, P95, and P99. The average hides tail behavior — a P99 of 30 seconds means 1% of users wait half a minute, even if P50 is 2 seconds. LLM latency is inherently variable because it scales with output length.
import time
import statistics
from collections import deque
class LatencyTracker:
def __init__(self, window_size=1000):
self.samples = deque(maxlen=window_size)
def record(self, latency_ms):
self.samples.append(latency_ms)
def percentile(self, p):
if not self.samples:
return None
sorted_samples = sorted(self.samples)
idx = int(len(sorted_samples) * p / 100)
return sorted_samples[min(idx, len(sorted_samples) - 1)]
def report(self):
if not self.samples:
return {}
return {
'count': len(self.samples),
'p50_ms': self.percentile(50),
'p95_ms': self.percentile(95),
'p99_ms': self.percentile(99),
'max_ms': max(self.samples)
}
tracker = LatencyTracker()
for ms in [1200, 1100, 1300, 1150, 8500, 1200, 1250, 15000, 1100, 1300]:
tracker.record(ms)
print(tracker.report())All lessons in this course
- Caching Strategies for Prompts
- Batch Processing and Async Execution
- Load Balancing Across Models
- Monitoring and Alerting for Prompt Pipelines