There are two kinds of autoscaling configurations: the ones that make for a great demo, and the ones that survive production. This session shares lessons from autoscaling strategies proven across thousands of production Kubernetes clusters.

Drawing from a broad KEDA user base, the session covers practical patterns for fine-tuning autoscaling behavior while improving resiliency: combining signals, avoiding noisy or overly reactive scaling decisions, and reducing the load autoscaling queries place on centralized monitoring systems through metric caching with OpenTelemetry.

In complex microservice architectures, autoscaling often spans multiple applications and infrastructure layers that must react in coordination. This session will discuss chained scaling patterns and practical ways to reduce delays between dependent scaling decisions, so teams can keep reliability and infrastructure costs predictable in production.