Cloud and AI Infrastructure Cost Optimization: A Comprehensive Review of Strategies and Case Studies
Saurabh Deochake
TL;DR
The paper surveys cloud and AI infrastructure cost optimization, linking pricing models to practical strategies across compute, storage, network, and logging. It highlights how AI workloads have shifted cost dynamics, especially GPU and inference expenses, while documenting notable cost savings from architectural reengineering, platform migrations, and pricing-model alignment in real-world cases. Key contributions include a taxonomy of pricing models, a comprehensive set of optimization techniques, and forward-looking research directions in automated FinOps, AI-specific cost management, and sustainability. The findings underscore that substantial savings (28-90%) are achievable through deliberate cost governance, strategic resource choices, and leveraging newer pricing mechanisms, with significant implications for both enterprises and cloud providers.
Abstract
Cloud computing has revolutionized the way organizations manage their IT infrastructure, but it has also introduced new challenges, such as managing cloud costs. The rapid adoption of artificial intelligence (AI) and machine learning (ML) workloads has further amplified these challenges, with GPU compute now representing 40-60\% of technical budgets for AI-focused organizations. This paper provides a comprehensive review of cloud and AI infrastructure cost optimization techniques, covering traditional cloud pricing models, resource allocation strategies, and emerging approaches for managing AI/ML workloads. We examine the dramatic cost reductions in large language model (LLM) inference which has decreased by approximately 10x annually since 2021 and explore techniques such as model quantization, GPU instance selection, and inference optimization. Real-world case studies from Amazon Prime Video, Pinterest, Cloudflare, and Netflix showcase practical application of these techniques. Our analysis reveals that organizations can achieve 50-90% cost savings through strategic optimization approaches. Future research directions in automated optimization, sustainability, and AI-specific cost management are proposed to advance the state of the art in this rapidly evolving field.