This study investigates whether cloud provider solutions offer cost advantages over private cloud infrastructures in large-scale government settings. Focusing on the case of Greek Governmental Cloud (GCloud), we conduct a comprehensive cost comparison across three deployment models: the existing on-premise infrastructure, an IaaS cloud provider solution and a PaaS cloud provider solution. The cost analysis considers a five-year operational time window and uses real procurement data and updated discount policies. We take into consideration that the organization already has data center infrastructure and explores the migration options to cloud provider solutions. The cost analysis findings show that although provider solution can match or slightly undercut on-premise costs under specific conditions where agility is important, there is no financial advantage regarding standard operation needs. This real-world case study of the Greek Government Cloud highlights that cloud migration decisions for large organizations, with existing IT infrastructures, should be taken after thorough cost evaluation, accurate workload analysis, and a deep understanding of dynamic pricing models. Moreover, this study offers practical recommendations to decision-makers that navigate complex infrastructure choices. While infrastructure choices involve factors such as performance, security, and operational flexibility, this study intentionally focuses solely on the cost, aiming to provide a clear and quantifiable economic comparison. In general, this study aims to open a discussion about whether Cloud solutions are a panacea, especially in government settings.

Large Language Models (LLMs) are pivotal in generative AI applications. Consequently, major cloud providers, such as Amazon, Azure, and Google, introduce the offering of LLM-as-a-Service (LLMaaS) products to enable businesses to leverage NLP, data analysis, and predictive modeling in their cloud solutions. This paper explores the incorporation of LLM-as-a-Service solutions into business workflows with a focus on inference costs. We review various LLMaaS offerings and conduct a comparative analysis based on a real-world case study of an AI chatbot.

Cloud computing is a growing industry and it has already dominated many IT markets segments. Cloud providers offer numerous equivalent IaaS services aiming to fulfill clients' requirements. In addition, cloud clients need to choose solutions that minimize costs without compromising efficiency though. However, not only the confusion due to the large variety of cloud services but also the uncertainty about the efficiency specifications that their cloud services should have, can make cloud computing services selection a difficult task for the users. Into this context, this paper presents an approach to a multi-attribute decision-making problem that focuses on the calculation of efficiency of IaaS cloud services as a measurable driver for both clients and providers. A DEA input-oriented model is described, which estimates efficiency of cloud services based on functional and non-functional parameters. Furthermore, the contribution of functional and non-functional features to the overall performance is examined. This innovative model urges providers to optimize the efficiency of their services aiming to increase their market share and, at the same time, assists clients in choosing a cost-effective cloud solution.