MANAGING DATA TRANSFER COSTS IN HYBRID CLOUD INFRASTRUCTURES: APPLIED RESEARCH
Hybrid cloud infrastructures offer organizations the flexibility to integrate on-premises resources with public cloud services; however, this integration introduces substantial challenges related to data transfer costs. Charges associated with data egress, inter-region communication, and continuous data replication can escalate rapidly, particularly in multi-cloud scenarios where workloads and datasets are distributed across heterogeneous environments. As a result, unmanaged data movement has the potential to undermine the economic benefits typically associated with cloud adoption. This study investigates strategies for managing and optimizing data transfer costs in hybrid cloud ecosystems, addressing architectural, operational, and financial dimensions. Key approaches include optimizing data locality to ensure that workloads are executed in proximity to their associated datasets, leveraging dedicated interconnect services to achieve predictable bandwidth and reduced egress fees, and applying techniques such as caching, compression, and deduplication to minimize data transfer volumes. Furthermore, workload placement policies and FinOps practices—supported by automation and policy-as-code mechanisms—are identified as critical enablers for enforcing cost-efficient operations and maintaining financial governance. The findings highlight that proactive monitoring, combined with cost-aware architectural design, is essential to balancing performance requirements with economic sustainability. Organizations that embed cost optimization principles into the design and operation of hybrid cloud infrastructures are better positioned to achieve long-term efficiency, scalability, and financial resilience.