Performance Monitoring Setup
Monitor LLM performance in real-time. Track latency, errors, costs, and usage patterns with comprehensive observability tools.
Metrics Tracked
50+
Performance indicators
Alert Response
<30s
Average detection time
Data Retention
90 days
Historical analysis
OpenTelemetry Integration
from opentelemetry import trace, metrics from opentelemetry.instrumentation.requests import RequestsInstrumentor # Initialize tracing tracer = trace.get_tracer(__name__) meter = metrics.get_meter(__name__) # Create metrics latency_histogram = meter.create_histogram( name="llm_request_duration", description="LLM request latency", unit="ms" ) error_counter = meter.create_counter( name="llm_errors_total", description="Total LLM errors" ) class MonitoredLLMClient: @tracer.start_as_current_span("llm_request") def complete(self, prompt): span = trace.get_current_span() span.set_attribute("model", self.model) span.set_attribute("prompt_tokens", len(prompt)) start_time = time.time() try: response = self.client.complete(prompt) latency = (time.time() - start_time) * 1000 latency_histogram.record(latency) return response except Exception as e: error_counter.add(1) span.record_exception(e) raise
Key Metrics to Monitor
Performance Metrics
- Request latency (P50, P95, P99)
- Tokens per second
- Time to first token
- Queue depth
Business Metrics
- Cost per request
- Error rates by model
- Usage by endpoint
- Cache hit rates
ParrotRouter provides built-in monitoring with Prometheus-compatible metrics and real-time dashboards. No additional setup required.
References
- [1] arXiv. "Efficient LLM Inference" (2024)
- [2] Hugging Face. "GPU Inference Optimization" (2024)
- [3] NVIDIA. "LLM Inference Optimization" (2024)