Latency metrics quantify the time delays an AI system introduces from user request to final response. Accurate measurement and interpretation guide capacity planning, SLA definitions, and user-experience improvements.
Definition and Scope
Latency is typically decomposed into several sequential components:
- Network transit (client ↔ edge POP)
- Load-balancer queuing
- Model orchestration overhead (auth, routing, feature fetching)
- Inference runtime (GPU/CPU execution)
- Post-processing and serialization
Common Metrics
Measurement Best Practices
- Collect server-side timestamps for every pipeline stage; client probes mis-attribute network jitter.
- Bucket metrics by model, region, and request shape (context length) to expose hotspots.
- Record cold vs warm starts separately—mixing hides provisioning bugs.
- Align clock sources with NTP to <1 ms skew.
Design Trade-offs
- Aggressive batching improves throughput but increases p99 queue latency.
- Compression shrinks payloads at the cost of CPU time.
- Early-exit streaming cuts perceived TTFT yet may under-utilize GPUs when output is short.
Current Trends (2025)
- End-to-end distributed tracing (OpenTelemetry) adopted across inference stacks.
- GPU direct-reply: kernels stream tokens over RDMA, shaving 35 ms Host↔Device hops.
- Predictive autoscaling driven by quantile regression forecasts lowers p99 by 22 % in production1.
Implementation Tips
- Expose latency histograms, not just averages, on dashboards.
- Alert on p95 SLA breaches for >5 min to ignore transient spikes.
- Include prompt token count in logs to correlate with decode time.