To address scalability challenges in system design, I would: 1. Assess Bottlenecks: Identify and analyze areas in the system where performance slows under high load, such as databases or specific services. 2. Implement Load Balancing: Distribute traffic evenly across instances using load balancers to prevent any single point from becoming a bottleneck. 3. Optimize Resource Allocation: Use dynamic scaling to add or remove resources based on demand. 4. Microservices and Caching: Migrate critical functionalities to microservices to decouple dependencies and implement caching to reduce repeated computations. 5. Database Sharding: For data-heavy applications, use sharding to distribute data across multiple databases.

Jumping straight into scalability without assessing efficiency of services can be suboptimal and costly. Any system can be scaled but would the cost to scale justify the benefits? Over time, efficiency of most systems reduce during maintenance. Their initial capacity may be sufficient for these inefficiencies not to be noticed. But a substantial load increase makes them noticeable. First step should be to profile the system to look for inefficiencies that can be easily removed, past which scaling will be more cost effective. Then refactoring a monolithic system into domain driven modular, decoupled and stateless services enables more granular scaling where only the bottlenecked services are scaled, making scaling more cost effective.

To tackle scalability issues in system design, assess architecture limitations with TOGAF and use capacity planning to project resource needs. Adopt a microservices model with Kubernetes for modular scaling, enabling each service to scale independently. Leverage cloud tools like AWS Auto Scaling or Azure Scale Sets for elastic resources, and use a hybrid cloud to scale select components cost-effectively. Apply database sharding and caching (e.g., Redis) to optimize data handling. Monitor with Prometheus and automate scaling to ensure smooth performance. Lean and Agile principles help prioritize high-impact scaling, balancing costs and flexibility.

At first understand current capacity than make a plan to use it efficiently(Load balancing) and also make a plan for future development.