Kubernetes autoscaling addresses fluctuating workload demands by dynamically adjusting pod replicas, container resources, or node count based on real usage patterns, enhancing application responsiveness and cost efficiency. Key methods include the Horizontal Pod Autoscaler (HPA), Vertical Pod Autoscaler (VPA), and Cluster Autoscaler (CA), each targeting different scaling needs, from pod-level to cluster-level adjustments. The introduction of Kubernetes v1.33 adds configurable tolerance to HPA, allowing for finer control over scaling sensitivity. Advanced strategies such as event-driven scaling with KEDA and multi-dimensional autoscaling further extend scalability options. Best practices emphasize selecting appropriate scalers for specific workloads, avoiding conflicts between HPA and VPA, and incorporating custom metrics. Tools like DevZero provide continuous optimization by dynamically adjusting resources, enhancing the autoscaling process beyond manual tuning, and linking scaling decisions to cost metrics, thus transforming reactive scaling into a continuous, efficient feedback system.