Autoscaling is an efficient method for managing load spikes on cloud platforms by dynamically adjusting the number of service instances based on current demand, as opposed to traditional vertical or manual horizontal scaling methods which can be cost-ineffective. The process involves using tools like Nomad Autoscaler to monitor metrics such as requests, memory, and CPU usage, and to automatically scale services up or down to match predefined targets. Key components of this system include Firecracker microVMs for service isolation, Prometheus for metric collection, and Kuma for balancing traffic across instances, ensuring a seamless distribution of load. Additionally, Nomad Autoscaler employs strategies like target-value and passthrough for scaling decisions and incorporates cooldown periods to prevent abrupt scaling changes. Future enhancements may include additional metrics for scaling, custom metrics, configurable cooldown periods, and cron-based scaling to accommodate predictable load patterns, all aimed at providing seamless, automated service scaling and deployment.