Finops in IT: Comparison between On-premise and Cloud Environments regarding Cost, Governance, and Scalability

Bruno César de Souza Dela Val Assis; Everton Gomede

DOI: 10.22167/2675-6528-202602742

Article derived from a Course Conclusion Work (TCC), with content based on the student’s original work and adapted to the editorial format of the E&S Magazine with the support of the ResumeAI tool, an artificial intelligence solution developed by Instituto Pecege for textual synthesis and organization.

Summary

The growing adoption of cloud computing has transformed the management of information technology resources in organizations, introducing challenges in cost control and financial governance. In this context, FinOps has emerged as an approach to integrate technical, financial, and business teams, promoting transparency and continuous optimization. The study comparatively evaluated the application of FinOps in on-premise and cloud environments, considering cost, governance, and scalability, and the impact of FinOps on the economic efficiency of cloud computing. The research adopted a quantitative, exploratory, and descriptive approach, based on computational cost simulation. Average market data were used to structure controlled and comparable scenarios, analyzing the financial impact of different infrastructure models over five years. The results indicated that the mere adoption of the cloud did not guarantee economic efficiency gains, with costs in environments without structured financial governance approaching the on-premise model due to waste. However, the incorporation of FinOps practices demonstrated a significant impact on cost optimization, reducing annual expenses and strengthening governance. It was concluded that the benefits of flexibility and scalability of the cloud are consistently materialized only with structured financial governance practices. The choice of infrastructure model should consider the workload profile and organizational maturity, prioritizing the implementation of FinOps from the initial stages to ensure continuous control and optimization.

Keywords: Cloud computing; Cloud computing; FinOps; Cost management; IT governance.

1. Introduction

Information technology (IT) plays a strategic role in contemporary organizations, being fundamental for innovation, operational efficiency, and the generation of competitive advantage. Traditionally, IT infrastructures were structured in on-premise environments, characterized by the acquisition and maintenance of proprietary physical assets, such as servers, storage systems, and network equipment. With the advancement of cloud computing, a relevant shift in this paradigm is observed, driven by characteristics such as elasticity, scalability, and speed in resource provisioning.

This new computing model allows for greater flexibility in the use of computational resources, reducing the need for high initial investments and enabling greater adaptation to demand variations. The literature indicates that the cloud offers significant advantages in terms of scalability and operational efficiency, while the on-premise model tends to present greater control over resources and greater cost predictability, especially in scenarios with stable workloads (Marston et al., 2011; Gartner, 2022). However, cloud adoption also introduces relevant challenges, especially regarding financial management.

Unlike the traditional model, based mostly on capital expenditures (CAPEX), cloud computing operates under an operational expenditures (OPEX) model, where costs vary according to consumption. In this scenario, several organizations have started to face difficulties related to cost control, budget predictability, and the alignment between IT costs and business value. Studies indicate that cloud environments without structured governance practices can present significant levels of waste, mainly due to idle instances, over-provisioning, and the absence of continuous monitoring (Flexera, 2023; FinOps Foundation, 2023).

In this context, FinOps (Financial Operations) emerges, an approach that integrates technical, financial, and business teams with the objective of promoting greater transparency, shared responsibility, and continuous cost optimization in cloud computing environments. FinOps proposes a paradigm shift in IT financial management, moving the focus from centralized controls to data-driven governance based on near real-time consumption (FinOps Foundation, 2023; Stormont, Fuller, and Fuller, 2021). Recent literature also highlights that maturity in adopting FinOps practices is directly related to organizations’ ability to align infrastructure costs with business objectives, evolving from initial control stages to more advanced levels of optimization and governance (FinOps Foundation, 2024).

Furthermore, IT financial governance plays a fundamental role in defining control mechanisms and the strategic alignment of technological investments. According to Weill and Ross (2004), IT governance is essential to ensure the efficient use of resources and their orientation towards business objectives. Frameworks such as COBIT (ISACA, 2019) and ITIL (Axelos, 2019) establish guidelines for the alignment between technology and organizational management. However, such approaches do not fully contemplate the dynamic challenges of cloud computing, in which variable resource consumption requires continuous monitoring and data-driven decision-making. In this context, FinOps emerges as a complementary practice, focused on consumption-oriented financial governance.

Despite the advancement of literature on cloud computing cost management and the consolidation of FinOps as a financial governance practice, it is observed that a relevant portion of studies focuses on optimizing exclusively cloud environments. Approaches that perform a structured comparison between on-premise and cloud computing environments from multiple perspectives, such as cost, governance, and scalability, considering the same analytical scenario, are still limited. This gap limits the understanding of in which contexts FinOps adoption generates greater value and how its application impacts different infrastructure models. Given this context, this work seeks to answer the following research question: to what extent does the adoption of FinOps practices alter economic efficiency and financial governance when compared to the on-premise environment, also considering scalability aspects? It is hypothesized that the adoption of FinOps practices in the cloud computing environment is capable of reducing operational costs by an estimated range of 20% to 35%, in addition to improving financial governance and scalability levels, when compared to the on-premise model and the cloud environment without structured governance. On the other hand, in the absence of these practices, the cloud environment tends to present financial inefficiencies arising from uncontrolled resource consumption.

The relevance of this study lies in filling this gap, providing subsidies for strategic decision-making in organizations evaluating the migration or optimization of their IT infrastructures. Thus, the objective of this study is to comparatively evaluate on-premise and cloud models, considering the dimensions of cost, governance, and scalability, through a quantitative simulation based on market representative parameters.

2. Material and Methods

This study was characterized as an applied research, with a quantitative approach, of an exploratory and descriptive nature. The objective was to perform an analytical and comparative cost evaluation between on-premise and cloud computing environments, focusing on the application of FinOps practices. The methodology employed computational simulation, justified by its capacity to structure controlled and comparable scenarios, allowing for the isolated analysis of cost variables associated with different IT infrastructure models, in accordance with total cost of ownership (TCO) studies.

The methodological process was structured in a sequential pipeline, encompassing six main stages. Initially, the scenario definition and the establishment of premises that would guide the simulation were carried out. Subsequently, the cost modeling for the different environments was performed. The subsequent stages included the simulation of the proposed scenarios, the analysis of the obtained results, and, finally, a sensitivity analysis to verify the robustness of the conclusions.

The definition of the analyzed scenario was based on parameters representative of a medium-sized organization. A workload composed of virtual machines, data storage, and the continuous operation of critical corporate systems was considered. The values and assumptions used in the simulation were estimated from market averages and widely adopted benchmarks in the technology industry, according to technical reports and specialized literature (GARTNER, 2022; BRASSCOM, 2022; IDC, 2021; UPTIME INSTITUTE, 2021; FINOPS FOUNDATION, 2023).

The variables considered in the cost analysis included hardware acquisition, asset depreciation, electricity consumption, physical space and cooling, infrastructure maintenance, specialized labor, cloud operational costs, and scalability-associated costs. For the cloud environment without structured governance, a waste percentage of 25% was adopted, an intermediate value within a range of 20% to 35% indicated by the literature (FINOPS FOUNDATION, 2023; STORMONT; FULLER; FULLER, 2021).

Similarly, the estimated cost reduction with the adoption of FinOps practices was established at 30%. This percentage represented an intermediate maturity scenario, based on literature evidence pointing to gains between 20% and 35% (FINOPS FOUNDATION, 2023; FLEXERA, 2023). The choice of these intermediate values aimed to represent a realistic scenario of intermediate organizational maturity, avoiding extremes that could compromise the generalization of the simulation results.

The cost modeling was differentiated for on-premise and cloud environments, considering their operational and financial characteristics. In the on-premise model, costs were structured into initial investments (CAPEX) and recurring operational expenses (OPEX). CAPEX was distributed over a five-year analysis period through linear depreciation, aligned with IT asset accounting practices.

The calculation of the total annual cost for the on-premise environment followed the formula: Total annual cost = (Total CAPEX / useful life) + annual OPEX. For the cloud environment, the pay-as-you-go model was adopted, where costs varied according to resource consumption. The total annual cloud cost was calculated as: Total annual cost = monthly cost × 12.

Additionally, adjustments were incorporated to reflect waste and optimization. The waste cost was calculated as: Base cost × (1 + waste percentage). The cost with the application of FinOps was determined by: Waste cost × (1 – reduction percentage). The complete details of the formulas and premises are found in the appendices of the work.

The simulation was conducted considering a five-year time horizon, a period aligned with the depreciation practices of technological assets and the evaluation of IT infrastructure investments. For the cloud computing environment, two distinct scenarios were simulated: one without structured financial governance practices and another with the adoption of FinOps, allowing for the evaluation of the direct impact of these practices on cost optimization.

The analysis considered three scenario perspectives: a base scenario with average market parameters, a conservative scenario with lower operational efficiency, and an optimistic scenario with a higher level of resource optimization. For direct comparison purposes, the results presented focused on the base scenario, ensuring analytical consistency. The detailed calculation memo is found in the appendices of the work.

The data generated by the simulation were organized and analyzed using descriptive statistics. This technique allowed for the comparison between different infrastructure models from the perspectives of cost, governance, and scalability. The presentation of the results was carried out in a way that facilitates the visualization of the differences between scenarios and the impact of adopting FinOps practices on economic efficiency.

In order to evaluate the robustness of the results, a sensitivity analysis was performed. Variations in the main simulation parameters were considered, including different levels of waste (15%, 25%, and 35%) and different levels of cost reduction associated with FinOps (20%, 30%, and 35%).

Additionally, the sensitivity analysis included varying the workload, represented by the number of virtual machines and storage volume. This approach allowed for verification of the results’ behavior in the face of changes in the adopted premises, reinforcing the validity of the conclusions under different operational conditions. The detailed calculations of the sensitivity analysis are available in the appendices.

3. Results and Discussion

The results of the quantitative simulation, conducted to compare on-premise and cloud infrastructure models from the perspectives of cost, governance, and scalability, revealed important insights into the economic efficiency and management of IT resources. The analysis demonstrated that simply migrating to the cloud does not, in itself, guarantee efficiency gains, with the adoption of FinOps practices being a determining factor for cost optimization and the strengthening of financial governance. This study, therefore, offers an in-depth understanding of how different management approaches impact the financial and operational performance of technology infrastructure.

The research started from the characterization of a hypothetical scenario representative of a medium-sized organization, with approximately two hundred employees. The simulated workload included fifty virtual machines, twenty terabytes of storage, and the continuous operation of critical corporate systems, such as ERP, databases, and web applications. This configuration was established based on corporate infrastructure standards and market reports, aiming for an intermediate and comparable scenario for the analysis of the models (Gartner, 2022; IDC, 2021).

The financial analysis horizon was defined as five years, a period aligned with the depreciation practices of technological assets and the evaluation of IT infrastructure investments, according to Total Cost of Ownership (TCO) studies. For the simulation of cloud costs, waste percentages of 25% were considered in scenarios without structured financial governance, and a cost reduction of 30% with the adoption of FinOps practices. These intermediate values were chosen to represent a realistic scenario of organizational maturity, avoiding extremes that could compromise the generalization of the results.

Characterization of the analyzed scenario

The central premises that underpinned the construction of the simulated scenario included the organizational profile of two hundred employees, the utilization of fifty virtual machines and twenty terabytes of storage, with continuous operation (24×7) for critical systems. The analysis horizon was five years. For the cloud scenario without governance, a waste of 25% was estimated, while the adoption of FinOps resulted in a 30% reduction in costs. These parameters were derived from surveys in public cloud calculators, market research, and industry reports, such as those from FinOps Foundation (2023) and Flexera (2023), ensuring methodological consistency and adherence to market practices.

Costs in the On-Premise Model

In the on-premise model, initial investments (CAPEX) were estimated at R$ 1,100,000.00, covering the acquisition of servers, network equipment, storage, and software licensing. Considering the linear depreciation of these assets over five years, the annualized CAPEX was R$ 220,000.00. Recurring operational expenses (OPEX) were calculated at R$ 500,000.00 per year, including costs for electricity, cooling, hardware maintenance, physical space, and specialized labor. Thus, the estimated total annual cost for the on-premise environment reached R$ 720,000.00.

This annual cost of the on-premise model, although predictable, reflects the inherent rigidity of installed capacity, which does not always align proportionally with real demand. The fixed cost structure and the need for substantial initial investments limit flexibility and efficiency in scenarios of workload variation, as pointed out by Marston et al. (2011). Predictability, therefore, is an advantage that can be compromised by a lack of adaptability to changes in the business environment.

Costs in the Cloud Model

For the cloud environment, the base costs were estimated from resource consumption in the IaaS (Infrastructure as a Service) model, including computing, storage, and data traffic. The projected monthly cost was R$ 45,000.00, totaling R$ 540,000.00 annually. However, when considering an average waste percentage of 25% – resulting from idle resources, over-provisioning, and lack of governance, as indicated by the FinOps Foundation (2023) and Stormont, Fuller, and Fuller (2021) – the effective annual cost of the cloud environment without FinOps reached R$ 675,000.00.

This value of R$ 675,000.00 for the cloud environment without structured governance is remarkably close to the cost of the on-premise model (R$ 720,000.00). Such proximity highlights that the mere adoption of the cloud, without adequate financial control, may not generate the expected economic efficiency gains. The flexibility and scalability of the cloud, without active management, can easily lead to uncontrolled consumption and, consequently, high costs, as highlighted by Flexera (2023) reports.

Impact of FinOps Adoption in the Cloud

The incorporation of FinOps practices in the cloud environment has demonstrated a significant impact on cost optimization. Considering a conservative reduction of 30% in costs, resulting from actions such as rightsizing, shutting down idle resources, and using reserved instances, the annual cost of the cloud environment in the analyzed scenario became R$ 472,500.00. This reduction is calculated from the adjusted cost with waste (R$ 675,000.00), applying the optimization percentage.

This result of R$ 472,500.00 annually demonstrates that the economic benefits of cloud computing are directly conditioned by the adoption of adequate financial governance practices. FinOps, by promoting greater transparency, budgetary predictability, and strategic alignment between technology and business, becomes a determining factor for the sustainability and efficiency of the cloud model. The gains are not automatic, but depend on a continuous cycle of management and optimization, according to the FinOps Foundation (2024).

Consolidated Quantitative Comparison

The comparison of annual costs between the analyzed models revealed that the on-premise environment presents a cost of R$ 720,000.00. The cloud model without the adoption of FinOps resulted in a cost of R$ 675,000.00, while the cloud environment with FinOps achieved a cost of R$ 472,500.00. These data clearly demonstrate the economic advantage of FinOps, which provides a substantial reduction compared to the other two scenarios.

When analyzing the accumulated cost over a five-year horizon, it was observed that the on-premise model totaled R$ 3,600,000.00. The cloud environment without structured governance practices presented an accumulated cost of R$ 3,375,000.00, a still high value and close to the traditional model. However, the adoption of FinOps in the cloud environment resulted in an accumulated cost of R$ 2,362,500.00, showing savings of over R$ 1,000,000.00 in five years. This difference highlights the direct impact of FinOps on the economic efficiency of the infrastructure.

Analysis under the Perspectives of Governance and Scalability

Financial governance was evaluated based on criteria such as cost transparency, traceability, and monitoring frequency. In the on-premise model, governance tends to be more traditional, with less real-time visibility and greater rigidity in resource allocation. In contrast, the cloud model, especially when associated with FinOps, offers greater transparency and continuous monitoring capabilities, allowing for rapid adjustments and decisions more aligned with business objectives, according to the FinOps Foundation (2023).

Regarding scalability, the cloud environment presents significant advantages, enabling the expansion or reduction of infrastructure in an agile manner and without the need for initial investments. Provisioning time is reduced, and the ability to adapt to demand is high. In contrast, the on-premise model depends on prior planning and new investments to support demand growth, characterizing limited and less flexible scalability.

Comparative Synthesis

The comparative synthesis between the models reveals that, in terms of cost, the on-premise model and the cloud without FinOps present high costs, while the cloud with FinOps demonstrates a low cost. Budgetary predictability is high in on-premise (if there is no expansion), low in the cloud without FinOps, and medium in the cloud with FinOps. Scalability is limited in on-premise, but high in both cloud scenarios. Regarding governance and optimization, the on-premise model is traditional and restricted, the cloud without FinOps is low in both criteria, and the cloud with FinOps is high, highlighting the importance of financial management practices to maximize cloud benefits.

Critical Analysis of Results

The results obtained indicate that the adoption of cloud computing, in isolation, does not guarantee economic efficiency. It is observed that, in the absence of structured governance practices, costs can approach or even exceed those of the on-premise model. This behavior is consistent with studies from the FinOps Foundation (2023) and the Flexera State of the Cloud report (2023), which point to significant levels of waste in environments without structured financial control.

The literature converges with Stormont, Fuller, and Fuller (2021), who highlight that cloud flexibility, when not accompanied by governance mechanisms, can lead to inefficient resource utilization, reducing the expected economic benefits. Thus, the cloud ceases to be an intrinsically more efficient model and becomes directly dependent on organizational maturity in cost management. Economic efficiency is not exclusively associated with the technological model, but with the infrastructure usage profile.

On the other hand, the on-premise model can still present advantages in specific scenarios. Organizations with stable and predictable workloads tend to benefit from cost predictability, which aligns with classic IT infrastructure TCO analyses (Marston et al., 2011). This result reinforces the idea that the choice of infrastructure model should consider the workload profile and organizational maturity.

The analysis also shows that the impact of FinOps is more significant in organizations operating in dynamic environments, with multiple teams and decentralized resource consumption. This finding reinforces the view of the FinOps Foundation (2023), which positions FinOps as an essential discipline for consumption-oriented cloud computing environments. From a managerial perspective, the results suggest that decisions related to cloud adoption should consider not only technological aspects but also the organization’s ability to implement financial governance (McKinsey, 2021).

In this sense, the findings of this study converge with the literature by evidencing that the economic efficiency of the cloud is directly associated with the level of maturity in the organization’s financial management. FinOps should not be understood solely as a cost reduction practice, but as a data-driven and consumption-oriented governance model, capable of aligning technical and financial decisions, promoting greater transparency and shared responsibility.

Sensitivity Analysis

In order to assess the robustness of the results, a sensitivity analysis was performed, considering variations in the main simulation parameters. Different levels of waste (15%, 25%, and 35%), different levels of cost reduction associated with FinOps (20%, 30%, and 35%), and workload variations (number of virtual machines and storage volume) were analyzed. This approach allowed us to verify the behavior of the results in the face of changes in the adopted premises, reducing the dependence on specific values and strengthening the generalization of the conclusions.

Sensitivity to waste percentage

The sensitivity analysis to the percentage of waste in the cloud environment, keeping the FinOps cost reduction level constant at 30%, showed that the annual cost without FinOps ranges from R$ 621,000.00 (for 15% waste) to R$ 729,000.00 (for 35% waste). In contrast, the annual cost with FinOps ranged from R$ 434,700.00 to R$ 510,300.00, respectively. The annual savings generated by adopting FinOps increased from R$ 186,300.00 to R$ 218,700.00 as waste increased from 15% to 35%. This indicates that increased waste directly impacts total cloud costs, widening the gap between scenarios with and without governance, and that adopting FinOps maintains an economic advantage even in scenarios with lower waste levels.

Sensitivity to optimization level with FinOps

The variation in costs considering different levels of efficiency in adopting FinOps practices, keeping the waste percentage fixed at 25%, revealed that the annual cost with FinOps decreases as the maturity stage advances. With a 20% reduction (initial stage), the annual cost was R$ 540,000.00. For a 30% reduction (intermediate stage), the cost was R$ 472,500.00. In the advanced stage, with a 35% reduction, the annual cost reached R$ 438,750.00. These results indicate that the maturity level in FinOps implementation has a significant impact on total costs, with more expressive gains in more advanced optimization scenarios.

Sensitivity to workload variation

The sensitivity analysis to workload variation, considering different volumes of virtual machines and storage, while maintaining parameters of 25% waste and 30% reduction with FinOps, demonstrated that workload growth proportionally impacts costs in the cloud environment. For a reduced scenario (30 VMs / 10 TB), the annual cost with FinOps was R$ 294,000.00. In the base scenario (50 VMs / 20 TB), the cost was R$ 472,500.00. In an expansion scenario (80 VMs / 30 TB), the annual cost with FinOps reached R$ 724,500.00. The cloud’s cost structure maintains linear behavior, allowing for greater predictability and adaptation compared to the on-premise model, which would require new infrastructure investments to support expansion.

Sensitivity analysis synthesis

In general, the results of the sensitivity analysis indicate that, even with variations in the considered parameters, the cloud model with the adoption of FinOps practices maintains an economic advantage over scenarios without structured governance. In conservative scenarios, the cost difference between the models tends to decrease, while in scenarios with a higher level of waste and greater optimization capacity, the gains provided by FinOps become more significant. Additionally, workload variation reinforces the greater flexibility of the cloud model, highlighting its ability to adapt to demand growth without the need for high initial investments. Thus, the sensitivity analysis reinforces that the conclusions of this study are not conditioned to specific values adopted in the simulation, but rather to the structural dynamics of the analyzed models, especially regarding financial governance and continuous optimization capability.

In summary, the findings of this study demonstrate that the economic efficiency of cloud computing is not inherent to the technology, but rather a direct result of applying robust financial governance practices, such as FinOps. The comparison between on-premise, cloud without FinOps, and cloud with FinOps models revealed that, although the cloud offers superior flexibility and scalability, these benefits materialize into significant financial gains only when accompanied by active and continuous cost management. Thus, the adoption of FinOps emerges as a strategic differentiator for organizations seeking to optimize their IT investments and align technology with business objectives.

4. Conclusion

This study aimed to comparatively evaluate on-premise and cloud infrastructure models, considering the dimensions of cost, governance, and scalability. It was found that the mere adoption of cloud computing, without structured financial governance practices, does not guarantee economic efficiency gains, with annual costs approaching those of the on-premise model significantly due to waste. However, the incorporation of FinOps practices demonstrated a substantial impact on cost optimization, resulting in accumulated savings exceeding R$ 1,000,000.00 over five years in the simulated scenario. It was observed that, while the on-premise environment offers predictability for stable workloads, the cloud model, when associated with FinOps, provides greater flexibility, scalability, and transparency in financial management, essential elements for dynamic and consumption-oriented environments. Therefore, the economic efficiency of the cloud is directly conditioned by organizational maturity in managing its resources.

The main contribution of this work lies in providing analytical and managerial subsidies for strategic decision-making on IT infrastructure, by demonstrating that the economic feasibility of cloud computing is consistently materialized only with the implementation of structured financial governance practices, such as FinOps. It is recommended that organizations prioritize the adoption of FinOps from the initial stages of cloud migration, ensuring control, visibility, and continuous cost optimization. As a limitation, it is noted that the study was based on a simulated scenario with average market data, not contemplating specific particularities of real organizations, hybrid or multicloud architectures, nor indirect costs of security and compliance. For future studies, it is suggested to conduct empirical research with real data, expand the analysis to hybrid and multicloud environments, and incorporate variables of organizational maturity and regulatory aspects, aiming to improve the generalization and robustness of comparative analysis models in cloud computing.

Bibliographic References

AXELOS. ITIL Foundation: ITIL 4 Edition. London: TSO (The Stationery Office), 2019. Disponível em: https://www.axelos.com/certifications/itil-service-management. Acesso em: 10 fev. 2026.

BRASSCOM. Relatório de Mercado de Trabalho em Tecnologia da Informação no Brasil. São Paulo: Brasscom, 2022. Disponível em: https://brasscom.org.br. Acesso em: 12 fev. 2026.

FINOPS FOUNDATION. FinOps Framework: Cloud Financial Management. San Francisco: The Linux Foundation, 2023. Disponível em: https://www.finops.org/framework/. Acesso em: 20 jan. 2026.

FINOPS FOUNDATION. State of FinOps Report 2024. San Francisco: The Linux Foundation, 2024. Disponível em: https://www.finops.org/reports/state-of-finops/. Acesso em: 22 jan. 2026.

FLEXERA. 2023 State of the Cloud Report. Itasca: Flexera Software, 2023. Disponível em: https://info.flexera.com/CM-REPORT-State-of-the-Cloud. Acesso em: 18 jan. 2026.

GARTNER. Data Center Infrastructure Costs and Total Cost of Ownership Analysis. Stamford: Gartner Research, 2022. Disponível em: https://www.gartner.com/en/documents/4011501. Acesso em: 19 jan. 2026.

IDC. Worldwide Server Market Forecast 2021-2025. Framingham: International Data Corporation, 2021. Disponível em: https://www.idc.com/getdoc.jsp?containerId=US47581221. Acesso em: 19 jan. 2026.

ISACA. COBIT 2019 Framework: Introduction and Methodology. Schaumburg: ISACA, 2019. Disponível em: https://www.isaca.org/resources/cobit. Acesso em: 20 jan. 2026.

MARSTON, S.; LI, Z.; BANDYOPADHYAY, S.; ZHANG, J.; GHALSASI, A. Cloud computing – The business perspective. Decision Support Systems, v. 51, n. 1, p. 176-189, 2011. DOI: https://doi.org/10.1016/j.dss.2010.12.006.

MCKINSEY & COMPANY. Cloud’s trillion-dollar prize is up for grabs. New York: McKinsey Digital, 2021. Disponível em: https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/clouds-trillion-dollar-prize-is-up-for-grabs. Acesso em: 05 fev. 2026.

STORMONT, J. R.; FULLER, M.; FULLER, T. Cloud FinOps: Collaborative, Real-Time Cloud Financial Management. Sebastopol: O’Reilly Media, 2021. Disponível em: https://www.oreilly.com/library/view/cloud-finops-2nd/9781492098263/. Acesso em: 10 fev. 2026.

UPTIME INSTITUTE. Global Data Center Survey 2021. New York: Uptime Institute Intelligence, 2021. Disponível em: https://uptimeinstitute.com/about-ui/press-releases/uptime-institute-11th-annual-global-data-center-survey. Acesso em: 01 mar. 2026.

WEILL, P.; ROSS, J. W. IT Governance: How Top Performers Manage IT Decision Rights for Superior Results. Boston: Harvard Business School Press, 2004. Disponível em: https://store.hbr.org/product/it-governance-how-top-performers-manage-it-decision-rights-for-superior-results/3533. Acesso em: 05 mar. 2026.

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