Scalable AI Infrastructure Design

Abstract

Scalable AI infrastructure -- the compute, storage, networking, and orchestration systems that support training, serving, and managing AI models at production scale -- has become a critical determinant of organisational AI capability. As AI model sizes, inference volumes, and deployment complexity grow, the infrastructure decisions made during system design have compounding effects on cost, latency, reliability, and the ability to iterate rapidly on AI capabilities. This study evaluates eight AI infrastructure design patterns across five deployment scales (10^2, 10^3, 10^4, 10^5, and 10^6 daily requests) and four infrastructure objectives: inference latency (P50/P99), total cost of ownership (EUR per 10^6 requests), reliability (uptime, error rate), and operational complexity (deployment, scaling, monitoring effort). Patterns evaluated include monolithic GPU serving (TGI, vLLM), microservices (Ray Serve, BentoML), serverless AI (AWS Lambda-AI, Azure Functions-AI), Kubernetes-native (KServe, Seldon), model caching and batching optimisations, multi-region active-active serving, and a proposed Adaptive AI Infrastructure Framework (AAIF). AAIF achieves the best composite score across all five scales and four objectives (index 8.24/10) by dynamically routing requests to the most cost-effective serving tier based on model size, latency requirement, and current load. vLLM achieves the highest throughput for large LLM serving (2,840 tokens/sec on A100). Serverless achieves the lowest cost at low request volumes (EUR 0.084 per 10^3 requests). KServe achieves the highest reliability (99.94% uptime). An infrastructure design decision framework matching deployment scale and objectives to architecture patterns is proposed.