This study investigates the feasibility and performance of SLAM (Simultaneous Localization and Mapping) as a service (SLAM-as-a-Service) for outdoor augmented reality (AR) applications. Given the rapid advancements in AR technology, integrating lightweight AR glasses with real-time SLAM capabilities poses significant challenges, particularly due to the computational demands of SLAM algorithms and the limited hardware capacity of AR devices. This study proposes a scalable SLAM-as-a-Service framework that offloads intensive computational tasks to remote servers, leveraging cloud and edge computing resources. The ORB-SLAM3 algorithm, known for its robustness and real-time processing capabilities, was adapted and implemented in a service-oriented architecture. The framework was evaluated using the EuRoC dataset to benchmark processing speed, accuracy, and round trip time. The results indicate that while the proposed SLAM-as-a-Service model shows promise in handling high computational loads, several obstacles need to be addressed to achieve minimal round trip time and ensure a seamless AR experience. This thesis contributes to the development of scalable and efficient AR solutions by addressing the limitations of on-device processing and highlighting the potential of cloud-based services in enhancing the performance and feasibility of AR applications in dynamic outdoor environments.