Paper Title
Strategic Cost Analysis of Major Cloud Platforms for Large Language Model (LLM) Optimization
Article Identifiers
Registration ID: IJNRD_304795
Published ID: IJNRD2503319
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Keywords
Cloud Computing, Cost Optimization, Large Language Models (LLMs), Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP), AI Infrastructure, Spot Instances, Auto-scaling, TPU Optimization, Hybrid Cloud, Serverless Computing, Compute Instance Pricing, Storage Optimization, Data Transfer Costs, Workload Optimization, Cloud Pricing Models, GPU Utilization, Reserved Instances, Multi-cloud Strategy, Performance-Cost Tradeoff, Cloud-based AI Deployments, Strategic Cost Analysis Framework (SCAF), AI Infrastructure Economics, Enterprise AI Cost Management, GPU and TPU Cost Analysis, Cloud Resource Allocation, Scalability Strategies, Machine Learning Model Deployment, Energy-efficient AI Computing.
Abstract
Large Language Models (LLMs) are changing many areas, like natural language processing, enterprise AI, and generative AI applications. But their setup on cloud platforms has many money problems. Picking right compute instance, managing storage, controlling data transfer cost, and making workloads run better is big challenge. Running LLMs on big scale need proper check of cloud pricing models so cost stay low and performance does not go down. This research looks at price models and cost rules in three big cloud providers: Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). It studies important cost parts like compute instance choice, GPU and TPU price, storage type, network fees, and service-level agreement (SLA). From this, it finds best ways to make LLMs work better in cloud. For this, research use cloud pricing tools, expert knowledge, and real examples to make a **Strategic Cost Analysis Framework (SCAF)** for LLM use. This help company to decide cloud budget smartly and keep good performance. Also, research check cost-cutting methods like spot instance, reserved instance, auto-scaling, serverless model, and hybrid cloud. These helps reduce spending but still keep power needed for AI work. This study gives deep understanding on saving cost for LLM cloud use. It helps business, AI expert, and cloud engineer to run AI in cloud with better budget and still keep performance and growth good.
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How To Cite (APA)
Soniya Suthar, Dr. Vikas Tulshyan, Khusboo Rajput, Sourabh Dattalkar, & Jyoti Thakur (March-2025). Strategic Cost Analysis of Major Cloud Platforms for Large Language Model (LLM) Optimization. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 10(3), d188-d196. https://ijnrd.org/papers/IJNRD2503319.pdf
Issue
Volume 10 Issue 3, March-2025
Pages : d188-d196
Other Publication Details
Paper Reg. ID: IJNRD_304795
Published Paper Id: IJNRD2503319
Research Area: Science and Technology
Author Type: Indian Author
Country: Ahmedabad, Gujarat, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2503319.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2503319
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