The rapid expansion of big data demands compression algorithms that balance efficiency and effectiveness. However, selecting the appropriate algorithm is challenging due to inherent trade-offs between speed and compression ratio, further complicated by varying hardware environments. This paper presents Compression Scope, an interactive system for exploring these trade-offs through benchmarking, visualization, and analysis. The system can run predefined compression tasks on heterogeneous servers, demonstrate results through intuitive charts and highlight Pareto-optimal algorithms for data-driven decision-making, supporting aggregation, trend exploration, and exportable plots. By integrating automated benchmarking with interactive trade-off analysis, it empowers users to identify compression algorithms suited to their specific performance and resource constraints.