Choosing the Right Infrastructure Stack for Your AI Application: A Comprehensive Framework for Modern AI Systems

Abstract

The rapid growth of AI applications has fundamentally transformed how organizations approach system architecture and infrastructure design, creating a need for a comprehensive framework to select appropriate infrastructure components. This article presents a systematic methodology for understanding and implementing AI infrastructure through a layered architecture approach that decomposes the AI stack into six critical elements: (i) data and governance, (ii) storage systems, (iii) compute resources, (iv) model toolchains, (v) orchestration platforms, (vi) serving infrastructure including retrieval and augmentation systems, and observability and safety mechanisms. The article examines all six components along core AI system performance dimensions of quality, latency, throughput, cost, and energy efficiency, and analyzes inherent trade-offs. Unique bottlenecks and optimization strategies are identified for each infrastructure component, from data quality assurance challenges in governance layers to resource utilization inefficiencies in compute environments. This article presents a decision framework that encompasses workload characterization, technical evaluation criteria, economic analysis, and risk assessment, to enable AI practitioners to make informed infrastructure choices aligned with application-specific requirements and business constraints.