Performance Monitoring

Benched.ai Editorial Team

Performance monitoring tracks latency, throughput, GPU utilization, and error rates of AI services to ensure they meet SLOs.

Core Metrics

Metric	Description	Typical Target
p95 latency	End-to-end wall time	≤2 s chat
Time-to-first-token	Wait for first byte	≤200 ms
GPU utilization	Streaming multiprocessor busy %	≥70 %
5xx error rate	Failures ÷ requests	<0.1 %
Tokens / s	Decode speed	≥30

Observability Stack Layers

Layer	Example Tool
Metrics	Prometheus, Datadog
Distributed traces	OpenTelemetry, Jaeger
Logs	Loki, Elastic Stack
Alerts	PagerDuty, OpsGenie

Dashboards

Latency histogram (p50/p95/p99) per endpoint.
GPU utilization vs tokens/s scatter plot.
Error budget burn-down.

Current Trends (2025)

eBPF-based GPU exporters emit kernel-level latency.
Real-time $/token overlays show cost spikes.
AI-driven alert routing predicts which team owns an incident.

Implementation Tips

Propagate trace_id from gateway to model server.
Sample full traces at 0.1 % to control overhead.
Alert on SLO burn rate rather than raw thresholds.