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Research Paper | Information Technology | India | Volume 12 Issue 12, December 2023 | Rating: 2.6 / 10
AI-Driven Cloud Cost Management - AI Tools For Optimizing Cloud Resource Allocation and Costs
Sandeep Chinamanagonda
Abstract: In today's rapidly evolving digital landscape, cloud computing has become a cornerstone for businesses seeking scalability, flexibility, and efficiency. However, managing cloud costs effectively can be a complex challenge, especially as cloud environments grow in size and complexity. AI-driven cloud cost management is emerging as a game-changer, offering innovative solutions to optimize resource allocation and reduce expenses. By leveraging artificial intelligence and machine learning, businesses can gain real-time insights into their cloud usage, predict future costs, and automate decision-making processes. These AI tools can analyze vast amounts of data, identify underutilized resources, and recommend cost-saving measures that would be difficult to detect manually. Additionally, AI-driven systems can continuously monitor cloud environments, adjusting resource allocation dynamically to meet changing demands without overspending. This approach not only enhances cost efficiency but also improves operational performance by ensuring that cloud resources are used optimally. As more companies adopt multi-cloud and hybrid cloud strategies, the need for intelligent cost management solutions becomes even more critical. AI-driven cloud cost management offers a proactive and data-driven approach to navigating the complexities of cloud spending, enabling businesses to maximize their return on investment while maintaining control over their cloud expenditures. With AI at the helm, organizations can transform their cloud cost management from a reactive process into a strategic advantage, driving growth and innovation in an increasingly competitive marketplace.
Keywords: AI, cloud cost management, cloud resource allocation, AI-driven tools, cloud optimization, operational costs, cloud efficiency, cloud computing, machine learning, cost reduction strategies
Edition: Volume 12 Issue 12, December 2023
Pages: 2135 - 2149