This Paper is Part 1 of a FinOps for Data Center series outlining how applying FinOps Principles—focused on operational expenditure (OpEx), detailed cost attribution, and FinOps Framework Capability-driven practices—enables organizations to align Data Center investment with business value. By providing timely, accurate financial insights, FinOps empowers executive leadership to make more informed executive decisions.
The FinOps for Data Center Series
The Data Center is evolving from a passive cost center to a key enabler of business performance. FinOps provides a framework that allows organizations to:
By adopting FinOps, organizations can shift from fragmented infrastructure reporting to a comprehensive, data-informed model that aligns cost transparency with strategic goals.
This document provides high-level, vendor-agnostic FinOps guidance for Data Centers, outlining the scope of FinOps practitioners’ roles, the application of the FinOps Framework, and relevant theoretical and practical considerations. While FOCUS™ is referenced, detailed data-level information will be addressed separately.
This paper applies to FinOps practitioners who have been asked to manage technology spending that extends FinOps concepts beyond the scope of public cloud into Data Centers. Links to relevant material are contained in the Related FinOps Material section of the paper.
An existing understanding of the FinOps Framework Domains and Capabilities for public cloud, along with being familiar with the content and concepts in Part 2: FinOps for Data Center – Applying the FinOps Framework.
A FinOps Scope refers to a segment of technology-related spending where FinOps Practitioners apply FinOps concepts. FinOps Scopes extend the Framework’s operating model to encompass intersecting areas of technology spend, particularly as the practice evolves to include activities in addition to public cloud.
According to the State of FinOps 2025 survey, 22% of practitioners are currently engaged in managing Data Center costs, with expectations rising to 36% by 2026—a 14% increase. These trends suggest that FinOps is increasingly being applied to broader areas of technology spending beyond public cloud services.
By collaborating with Core and Allied Personas, FinOps Practitioners may help shift organizational culture away from traditional finance, procurement, and technology silos toward a more integrated, data-driven approach that supports planning, cost analytics, and optimization.
The purpose of this paper is to explore the FinOps Scope for Data Center and to support existing FinOps Practitioners in understanding this area and the application of the FinOps Framework.
In the context of FinOps Scopes, “Data Center” is a broad term used to describe non-cloud IT services delivered from facilities either directly owned or managed by the client through contractual or service agreements. The Data Center scope includes all technology-related spending and decision-making activities associated with planning, acquiring, operating, and optimizing the physical and virtual infrastructure that supports an organization’s technology needs.
The FinOps Data Center Scope is not confined to a particular facility type, commercial arrangement, or service delivery model. Rather, it represents a heterogeneous environment, with each Data Center exhibiting a unique set of characteristics, as outlined in the Perspectives section of this document.
With respect to physical premises, this paper does not distinguish between conventional Data Centers and other client-operated locations such as branch offices, factory floors, or remote sites like mines or any location used to host data or information processing resources is considered for the purposes of this paper. For accurate economic analysis and reporting of an end-to-end system, all components should be included regardless of physical location.
Practitioners will likely encounter a mix of technical architectures, commercial models, and delivery methods—often coexisting within a single facility. Effectively navigating these environments may require a flexible, system-by-system approach, recognizing the variability inherent in Data Center operations.
According to the US agency National Institute of Standards and Technology (NIST), for a service to be defined as “Cloud” it must possess the following 5 essential characteristics:
The key point: a service must exhibit all of these characteristics to be classified as a cloud service. By extension, if a service displays only some—or none—of these characteristics, it would not be considered a cloud service and may therefore be included in the Data Center Scopes document.
*NIST SP500-322
Private Cloud is one of the four cloud deployment models defined by NIST and warrants particular attention, as it represents a deployment approach that blurs the boundaries between Data Center and Public Cloud. As such, Private Cloud will be addressed in a separate FinOps Scopes working group publication.
For context, the four Cloud deployment models are:
As enterprises continue to evolve toward a more digital and cloud-centric landscape, the role of the Data Center is shifting—from a static operational function to a dynamic strategic asset. In the context of FinOps, the Data Center is no longer viewed solely as physical or virtual infrastructure, but as a defined segment of technology-related spending, shaped by business strategy and enabled by technology. This scope includes not only cost and usage management, but also the alignment of technology investments with broader organizational objectives, joining data center spending with that of cloud and other FinOps scopes
Data centers today face a range of challenges that may benefit from the application of FinOps concepts, including:
Without integrated financial oversight, Data Centers often face fragmented visibility, unplanned expenditures, and missed opportunities for innovation. Legacy cost management practices may lack the responsiveness required to meet modern business demands, making it challenging to shift from reactive cost reporting to proactive financial decision-making—and to strike a balance between rapid innovation and cost control.
FinOps offers a set of practical and actionable capabilities that may help address these challenges within the Data Center scope.
By applying FinOps concepts to the Data Center, enterprises can achieve more than cost control—they gain a framework for strategic alignment, operational excellence, and sustainable growth. This integrated approach supports the evolution of the Data Center from a traditional cost center to a key enabler of innovation and competitive advantage.
This paper introduces 16 key considerations for decision making when creating a practice profile about how to apply the FinOps Framework for Data Centers.
This considers how temporal dimensions influence financial decision-making in Data Center management. Infrastructure investments are typically evaluated across three distinct horizons:
The Time Horizons consideration aligns with the FinOps principle of “taking advantage of the variable cost model,” enabling organizations to balance fixed infrastructure investments against elastic cloud resources. This perspective transforms FinOps Forecasting by incorporating multi-cycle planning horizons into decision-making processes.
Reference: https://www.napkin.ai/ used to generate image.
Data Center infrastructure can be considered through functional layers—compute, storage, network, virtualization, and management—each with its own:
This decomposition enables more precise cost allocation across engineering teams and establishes a foundation for targeted workload optimization. By mapping infrastructure layers to cloud-equivalent services, organizations can create meaningful benchmarking across hybrid environments. Certain IaaS and PaaS services may also be deployed on-premises, requiring careful consideration when reconciling hosting-related costs with the consumption they generate.
This consideration supports the relationship between resource acquisition (procurement) and deployment (provisioning), highlighting the potential disconnect between purchasing cycles and operational requirements. Key components include:
It can be used to support workload optimization through demand-aligned procurement and rate optimization via strategic vendor engagement. It reinforces the FinOps principle that teams need to collaborate by promoting alignment among FinOps, procurement, and engineering functions.
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This provides consideration of the contrast between long-term infrastructure investments with flexible resource consumption models, addressing the following areas:
It can be used for balancing fixed and variable resources, when applying FinOps principles to traditional Data Center investments. It supports workload optimization by promoting commitment purchases only for baseline needs, while encouraging the use of elastic resources to accommodate variable workloads.
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This provides consideration of the financial implications of capital expenditures (CapEx) and operational expenditures (OpEx) in Data Center management—recognizing that both cost types are present in operating a Data Center.
It supports the FinOps principle “take advantage of the variable cost model” by guiding organizations to make informed decisions between fixed infrastructure investments and pay-as-you-go resources. It also enables more effective planning, estimating, and forecasting of technology investments.
Cost Flexibility and Scale-Up Feasibility:
For example, a Data Center owner managing global workloads may choose to selectively shut down certain clusters to reduce power and cooling costs. While the hardware remains a CapEx asset, OpEx expenses can be adjusted based on real-time demand. This approach supports a more proactive balance between CapEx and OpEx, enabling customized operations aligned with usage patterns.
Persona Involvement and Data-Driven Segmentation:
Personnel involved in day-to-day operations can provide valuable insights into how their activities impact overall efficiency. This increased operational awareness allows finance and engineering teams to strike the right CapEx-OpEx balance and improves planning responsiveness to workload increases or changing user demands.
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Considering a Data Center facility through the lens of physical infrastructure and operational environment, including:
Key Metric: Power Usage Effectiveness (PUE) = Total Facility Power / IT Equipment Power
This consideration supports cost allocation of facility-related expenses and contributes to sustainability initiatives through energy efficiency improvements. It aligns with the FinOps principle “everyone takes ownership” by highlighting how facility-level costs influence overall technology spending.
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This consideration focuses on implementing efficiency improvements across all aspects of Data Center operations.
Optimization Areas: Compute utilization, storage tiering, network traffic management, power efficiency, and license optimization
Key Metric: Optimization ROI = (Cost Savings + Performance Gains) / Implementation Cost
Optimization supports ongoing workload and rate optimization through targeted efficiency efforts. It aligns with the FinOps principle “business value drives technology decisions” by encouraging prioritization of initiatives based on return on investment and overall business impact.
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This consideration focuses on the consolidation and harmonization of cost data across hybrid infrastructure environments.
Integration Layers: Data aggregation, cost allocation, and temporal alignment
Key Metric: Integration Completeness = (Integrated Cost Sources) / (Total Cost Sources) × 100%
Cost Integrations enables holistic reporting and analytics across hybrid estates and supports accurate budgeting through unified cost visibility. It aligns with the FinOps principle “FinOps data should be accessible, timely, and accurate” by establishing a normalized, comprehensive view of technology spending.
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Total Cost of Ownership (TCO) offers a comprehensive view of all costs associated with acquiring, operating, and maintaining Data Center infrastructure over its full lifecycle.
TCO Elements: Hardware, software, facilities, personnel, maintenance, downtime, and security
Key Metric: TCO per Workload = (Total Costs Over Lifecycle) / (Number of Workloads)
This consideration supports accurate benchmarking across infrastructure models and enables informed planning and estimating for technology investments. It aligns with the FinOps principle “business value drives technology decisions” by providing a complete cost picture to support decision-making.
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This consideration focuses on examining the management challenges and resource overhead involved in maintaining Data Center operations.
Complexity Drivers: Hardware lifecycle management, multi-vendor environments, hybrid operations, and legacy systems
Key Metric: Operational Load Factor = (FTEs × Hourly Rate) / (Managed Infrastructure Value)
The Operational Complexity consideration can help in uncovering hidden costs and supports workload placement decisions through workload optimization. It aligns with the FinOps principle “teams need to collaborate” by emphasizing the cross-functional nature of operational management.
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This consideration focuses on Data Centers based on their environmental impact and resource efficiency.
Sustainability Metrics: Power Usage Effectiveness (PUE), Carbon Usage Effectiveness (CUE), Water Usage Effectiveness (WUE)
Key Metric: Sustainability Efficiency = (Workload Output) / (Environmental Impact)
The Sustainability consideration supports the integration of cloud sustainability considerations into financial decision-making and enables accurate benchmarking of environmental performance across infrastructure types. It aligns with the FinOps principle “business value drives technology decisions” by reinforcing the business case for sustainable operations.
Sustainability tracking in Data Centers typically spans three emissions scopes:
FinOps activities such as workload optimization and cloud sustainability efforts naturally reduce both costs and environmental impact. Engineers can view sustainability metrics as an additional lens to demonstrate the value of their efforts—when instances are right-sized, scheduling is applied, or cooling efficiency is improved, both financial and environmental performance are enhanced. This dual benefit can help build broader stakeholder support for FinOps initiatives.
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This consideration explores how company values, practices, and behaviors influence Data Center management and the adoption of FinOps practices.
Cultural Elements: Leadership commitment, cross-functional collaboration, risk tolerance, and an innovation mindset
Key Metric: FinOps Culture Index = (FinOps Initiatives Adopted) / (Total FinOps Opportunities) × 100
The Organizational Culture consideration supports effective implementation of FinOps Education & Enablement and enhances FinOps Practice Operations. It aligns with the FinOps principle “everyone takes ownership” by fostering a culture of accountability and shared responsibility for technology spending.
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This consideration focuses on identifying, managing, and eliminating inefficiencies in Data Center operations.
Physical Overprovisioning Waste Categories:
Unlike cloud environments, Data Centers often require a level of strategic overcapacity. It is important to distinguish this from wasteful overcapacity, ensuring the right balance is maintained.
Physical Lifecycle Waste Management: Includes proper decommissioning of deprecated hardware such as batteries, servers, racks, and cables.
Key Metric: Efficiency KPI = ($ Potential Savings from Identified Waste) / (Total IT Cost in Scope)
The Waste consideration supports in effective workload optimization by helping identify and eliminate idle resources. However, in physical environments, resource elimination typically occurs on a time-driven basis rather than through immediate automation. This perspective aligns with the FinOps principle “everyone takes ownership” by holding teams accountable for the efficient use of infrastructure and related resources.
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This consideration focuses on managing the complexities of environments that combine on-premises, colocation, private cloud, and public cloud infrastructure. When on-premises and cloud workloads coexist, the environment is considered hybrid; when the combination involves multiple Cloud Service Providers (CSPs), it is referred to as multi-cloud.
Hybrid Complexity Drivers: Workload placement, cost transparency, interoperability, and governance
Key Metric: Hybrid Cost Efficiency = (Total Business Value) / (Combined Infrastructure Costs)
The Hybrid Solutions consideration supports precise cost allocation across environments and informs strategic Architecting for Cloud decisions related to workload placement. It aligns with the FinOps principle “take advantage of the variable cost model” by supporting the balance between fixed infrastructure and elastic, consumption-based resources.
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This consideration focuses on examining how automated processes and orchestration tools can streamline Data Center operations. While automation is commonly applied in virtualized environments, enabling it for physical resource provisioning presents additional challenges. Tools such as Infrastructure as Code (IaC) have limited applicability in physical infrastructure, often resulting in pre-provisioned hardware to accommodate operational needs.
Automation Drivers: Cost management, operational efficiency, and governance
Orchestration Drivers: Workload placement and interoperability
Key Metric: Automation ROI = (Cost Savings + Efficiency Gains) / (Implementation Cost)
The Automation and Orchestration consideration supports continuous workload optimization and strengthens cloud policy and governance practices. It aligns with the FinOps principle “FinOps should be enabled centrally” by promoting the adoption of consistent automation frameworks across the organization.
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This consideration focuses on examining how infrastructure costs are allocated back to internal consumers across various infrastructure environments. On-premises Data Centers often rely on low-granularity allocation methods based on fixed percentages, leading to high administrative overhead. Colocation introduces improvements through space and power-based metrics, though typically requires manual processing. Hosting models enable service-based chargeback using provider invoices as a reference point. Private cloud environments support resource-based chargeback with automated metering. Public cloud provides the most advanced model, featuring consumption-based chargeback, automated allocation via tagging, real-time visibility, and integration with financial systems.
The Billing/Chargeback Model perspective is shaped by several key drivers:
Key Metric: Allocation Accuracy Index (AAI) = (Directly Attributed Costs / Total Infrastructure Costs) × 100%
The Billing/Chargeback consideration enables capabilities such as allocation, invoicing and chargeback, data ingestion, and reporting and analytics. The Allocation Accuracy Index helps measure maturity, ranging from lower attribution rates in traditional environments to 90% or higher in well-established cloud implementations.
See ‘Allocation’ Capability Consideration in the FinOps Framework section for further detailed information.
We’d like to thank the following people for their work on this Paper:
FinOps Practice Operations
