Cloud and AI Infrastructure Cost Optimization: A Comprehensive Review of Strategies and Case Studies
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866915756549079040 |
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| author | Deochake, Saurabh |
| author_facet | Deochake, Saurabh |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2307_12479 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Cloud and AI Infrastructure Cost Optimization: A Comprehensive Review of Strategies and Case Studies Deochake, Saurabh Distributed, Parallel, and Cluster Computing Computational Engineering, Finance, and Science Systems and Control General Economics Economics K.6.0 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. |
| title | Cloud and AI Infrastructure Cost Optimization: A Comprehensive Review of Strategies and Case Studies |
| topic | Distributed, Parallel, and Cluster Computing Computational Engineering, Finance, and Science Systems and Control General Economics Economics K.6.0 |
| url | https://arxiv.org/abs/2307.12479 |