In the realm of modern cloud-native architecture, auto-scaling and load balancing play crucial roles in ensuring high availability and performance, though they address different challenges. Auto-scaling adjusts the number of compute instances based on traffic or performance metrics, facilitating capacity planning, cost optimization, and fault tolerance, primarily through horizontal scaling. In contrast, load balancing distributes incoming traffic across multiple instances or services to enhance availability, reduce latency, and support fault tolerance, with Layer 4 handling routing based on IP and port, and Layer 7 making more content-aware decisions. In a practical scenario, such as a REST API backend on AWS EKS, an AWS ALB distributes traffic to backend pods, with Kubernetes HPA scaling the pod count in response to metrics like CPU usage or request latency. Best practices include using load balancers even with a single instance for easier scaling, configuring graceful termination to prevent errors, and monitoring performance with tools like Prometheus and Grafana. Additionally, services like PubNub optimize load balancing and auto-scaling by offloading real-time communication to its edge network, allowing backend services to focus on business logic and scale based on specific events rather than sheer connection volume, as demonstrated in applications like real-time collaboration tools and IoT monitoring platforms.