Cloud optimization involves creating a lean, fault-tolerant architecture that is scalable, cost-efficient, and driven by automation and data. Key principles include using microservices for independent scaling, decoupling systems to reduce dependencies, and employing elasticity for dynamic resource scaling. PubNub plays a crucial role in enabling real-time, event-driven communication between decoupled services, supporting microservices and edge applications with its global infrastructure. Effective cloud cost management requires profiling usage patterns and enforcing accountability through resource tagging, which aids in budget adherence and infrastructure decision-making. Autoscaling strategies must distinguish between stateless and stateful workloads, using tools like Kubernetes HPA for efficient scaling based on application-specific metrics rather than just system-level ones. Cloud capacity planning involves balancing on-demand and spot instances to optimize cost and reliability, with PubNub facilitating real-time signaling and backend decoupling. Storage and delivery optimization focus on balancing performance and cost with efficient caching strategies across edge, application, and database layers, where PubNub excels in real-time use cases.