Operational Efficiency and Optimization for GenAI Applications
Performance Engineering and GenAI Observability
Measure end-to-end and stage-level latency, token throughput, retrieval quality, errors, throttles, safety outcomes, quality, cost, and business impact with traces, logs, metrics, and alerts.
Concepts
- Time to first token and tokens per second explain streaming experience better than one aggregate latency number.
- Trace retrieval, reranking, model invocation, tools, and postprocessing to localize slow or failing stages.
- Operational dashboards should pair latency and error metrics with quality, safety, cost, and business outcomes.
- Model invocation logging can contain sensitive content and therefore needs access and retention controls.
- Bedrock invocation logging is disabled by default and configured per account and Region; request metadata and delivery metrics make releases attributable and logging failures visible.
Exam tips
- CloudWatch provides metrics, logs, dashboards, alarms, and anomaly detection.
- Streaming reduces perceived latency but does not necessarily reduce total generation time.
- Alert on leading indicators such as throttling and token bursts as well as user-visible failures.