In the era of digital transformation, cloud-native applications have become the cornerstone of scalable, resilient, and flexible software solutions. API-based integration architectures are pivotal for seamless communication between microservices and third-party systems in cloud environments. However, the widespread adoption of these architectures introduces significant cost challenges, driven by data transfer fees, API call overheads, resource consumption, and multi-cloud interactions. This research paper explores various cost optimization strategies tailored for API-based integration in cloud-native applications. Key approaches include API call optimization, data transfer minimization, efficient resource provisioning, and the use of caching and compression techniques. Additionally, the study examines real-world scenarios to highlight the impact of cost-saving measures and the role of advanced analytics in forecasting API usage patterns. By adopting these strategies, organizations can enhance cost efficiency without compromising on performance or scalability. The findings contribute to the broader understanding of sustainable cloud-native application development, ensuring that API-driven solutions remain economically viable while supporting rapid innovation and growth.