Hybrid Cloud-Edge Data Pipelines: Balancing Latency, Cost, and Scalability for AI

Sai Prasad Veluru

Authors

Sai Prasad Veluru Sai Prasad Veluru Author

Keywords:

Hybrid cloud, edge computing, AI data pipelines, latency optimization, cost efficiency, scalability, real-time analytics, cloud-edge orchestration, distributed AI, Internet of Things (IoT), edge inference, data synchronization, model deployment, resource allocation, intelligent systems, fog computing, AI infrastructure, edge devices, cloud-native platforms, sensor networks

Abstract

Conventional centralized cloud systems are more progressively challenged to meet the performance & also cost criteria of actual time processing as AI applications become more data-intensive and also latency-sensitive. This has started a developing shift towards hybrid cloud-edge data pipelines, which deliberately combine the agility of edge systems with the scalability of cloud computing. The need of such architectures for AI workloads—including smart cities, autonomous automobiles & more industrial IoT—as well as the important technical and more architectural elements required in their development are investigated in this paper. We clarify the main goals of this hybrid approach, which consists in the need to reduce latency, lower bandwidth utilization, and preserve their data privacy close to the source. The talk stresses the necessary trade-offs among latency, cost, and also scalability, therefore offering sensible analysis of how these factors affect architectural decisions. With an eye toward intelligent segmentation, processing & orchestration of data across edge devices and cloud platforms, this case study examines the construction of a hybrid pipeline for an AI-driven analytics system. The results underline the need for more adaptive resource allocation, containerized workloads, and actual time data synchronizing in reaching a balance that satisfies both performance & also financial constraints. The paper presents a realistic perspective on how businesses may address the complex but advantageous challenge of building durable, scalable, sufficiently adaptable hybrid AI data pipelines that can adapt to changing technological needs.

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