Grafana Labs has enhanced the scalability and efficiency of its log aggregation system, Grafana Loki, by implementing the Kubernetes-based Event Driven Autoscaler (KEDA) to manage the fluctuating demands of querier components within Grafana Cloud Logs. This move addresses previous challenges of manual scaling, such as delays, over-provisioning, and operational toil, by automating the scaling process based on specific metrics like the number of inflight requests. KEDA, which integrates with Prometheus metrics and is built on top of Kubernetes' Horizontal Pod Autoscaler (HPA), allows for more responsive and cost-effective scaling decisions. By fine-tuning thresholds and stabilization windows, Grafana Labs achieved smoother scaling transitions, reduced the average number of replicas, and maintained query latency without degradation. This approach not only lowers operational costs but also enhances the reliability and performance of Grafana Loki, making it more user-friendly and cost-efficient for various deployment scales.