One Thing that I have found helpful is to use pay per use services while choosing services in cloud architecture. This way we can limit the usage of the resources and use only that is required. The above method help in: 1. Better performance by reducing the ram/ memory usage 2. Simpler architecture for simpler project 3. Reduces cost of development 4. Easy monitoring as the resources are limited. Hope this answer helps out the coder community. Happy coding!!!

Keep an eye on the autoscaling setup to ensure it handles workloads as expected. If any performance issues or unexpected scaling behavior occur, adjust instance limits, cooldown periods, or scaling policies to better match demand.

I would prefer - Using monitoring tools like AWS CloudWatch, Azure Monitor or Google Cloud Operations Suite to identify recurring bottlenecks and adjust resource allocation or optimize code to address inefficiencies. - Autoscaling to handle fluctuating demands seamlessly and choose storage or compute solutions aligned with workload requirements.

These are strategies I would prefer. 👇 - Right-size your cloud resources by matching instance types and sizes to workload requirements. - Leverage load balancing techniques to distribute traffic evenly across resources. - Implement a multi-cloud or hybrid cloud strategy to optimize performance and reduce vendor lock-in.

- Monitor continuously with APM tools - Optimize compute, network, storage, and database layers - Use auto-scaling & caching for performance gains - Optimize cost & resource allocation to prevent over-provisioning