Integrating Hybrid Cloud and Serverless Architectures for Scalable AI Workflows

Pavan Srikanth Subba Raju Patchamatla

doi:10.15662/IJRAI.2023.0606004

Authors

Pavan Srikanth Subba Raju Patchamatla Cloud Application Engineer, RK Infotech LLC, USA Author

DOI:

https://doi.org/10.15662/IJRAI.2023.0606004

Keywords:

Hybrid cloud, serverless computing, AI workflows, scalability, workload orchestration, elasticity, cost optimization, fault tolerance

Abstract

The growing complexity of artificial intelligence (AI) applications demands cloud architectures that can efficiently balance scalability, cost, and flexibility. This paper explores the integration of hybrid cloud infrastructures and serverless computing models to enable scalable AI workflows across heterogeneous environments. Hybrid cloud provides the ability to distribute workloads between private and public clouds, optimizing for performance, compliance, and resource availability. Serverless architectures complement this by enabling dynamic scaling, fine-grained resource allocation, and reduced operational overhead. Together, these paradigms create a unified framework for AI training, inference, and data processing, ensuring elasticity while minimizing costs. The research evaluates workload orchestration strategies, latency performance, and fault tolerance in hybrid serverless deployments. Findings demonstrate that combining hybrid cloud and serverless approaches enhances workflow efficiency, accelerates model deployment, and improves resilience, offering an effective blueprint for organizations aiming to operationalize AI at scale.

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