What is cloud financial management?

While organizations might use CFM to reduce cloud costs, the strategy also promotes increased innovation capacity, improved security and resiliency, accelerated speed to market and more.

By 2027, an estimated 90% of enterprises will use a hybrid cloud environment (a unified IT infrastructure that combines public, private and on-premises services and components), according to Gartner. This approach gives teams the flexibility to provision and scale resources on-demand, speeding up workflows and promoting independence. For instance, departments can add or eliminate services with a few clicks.

But intricate cloud environments also make spending and operations more difficult to track, potentially leading to runaway costs, security gaps, incompatibility issues and other problems. Monthly cloud bills for some large enterprises now contain hundreds of millions of line items—enough to break a traditional spreadsheet platform.

Without a comprehensive strategy to analyze these metrics, companies might struggle to make cost-effective decisions. For example, if there are no cost transparency mechanisms in place, an organization might be unable to find the origin of an unusual usage spike, resulting in a costly, time-intensive troubleshooting process.

Cloud inefficiencies are a growing problem: Gartner predicts that global public cloud spending will reach USD 723.4 billion in 2025, marking an approximate 21% jump from 2024. Meanwhile, organizations report that some 24% of their cloud software spending ultimately goes to waste, cutting into innovation, infrastructure and security budgets.

CFM aims to reduce these risks with robust governance and oversight strategies (such as centralized monitoring and enforcement, financial accountability frameworks and automated alerting) while taking advantage of the dynamic and adaptable nature of modern hybrid and multicloud environments. The framework also encourages collaboration between IT, finance and business operations, helping ensure that each department is aligned around a shared set of business outcomes and financial goals.

CFM strategies enable organizations to anticipate how new initiatives or programs might affect cloud usage in advance instead of scrambling to respond after the fact. With a clear understanding of how their cloud environment operates, teams can make informed decisions—proactively scaling resources, managing costs and responding to errors with greater agility and confidence.

Cloud cost management is a traditional strategy concerned primarily with managing and reducing cloud spend. It prioritizes cost visibility and reporting, resource tagging and cost allocation methods such as chargeback and showback to give organizations a comprehensive view of their cloud spending. However, it does not focus heavily on company-wide cultural shifts. A related term, cloud cost optimization, refers to the actions a company takes in response to its cloud cost management strategy.

Built on the foundations of cloud cost management, CFM is broader in scope and more resilient to the complexities of multicloud and hybrid environments. Rather than reacting to cloud bills, CFM anticipates cloud usage in advance with forecasting, statistical modeling and other techniques. While cutting cloud expenditures can be one goal, CFM also focuses on maximizing business value, improving accountability and fostering communication between teams. Many companies adopt a CFM framework during the cloud migration process, when an organization moves its data and services from an on-premises data center to the cloud, or as part of an organization-wide digital transformation strategy.

A third approach called financial operations (FinOps) shares significant overlap with CFM. FinOps can be thought of as a modern variant of CFM that places as much emphasis on aligning company culture around efficient practices as it does on the technical aspects of cloud operations. FinOps itself is inspired by DevOps: In the same way that DevOps revolutionized software development by breaking down silos and increasing agility, FinOps seeks to maximize the business value of cloud by bringing together technology, business and finance professionals under a shared set of processes.

Another distinction is that, while CFM derives from multiple perspectives and management philosophies, FinOps best practices are standardized and promoted through the FinOps Foundation, a nonprofit founded in 2019. The organization uses a maturity model to assess enterprises’ progress in adopting FinOps principles, with benchmarks and guidance for how to move through each stage. However, as cloud adoption becomes more ubiquitous among major enterprises, IT leaders increasingly use the terms FinOps and CFM interchangeably, despite subtle differences.

Cloud financial management frameworks rely on multiple, overlapping strategies to track cloud expenses, identify usage patterns, maximize returns on investment and anticipate future scenarios.

CFM strategies aim to make enterprises aware of the mechanisms that drive costs and proactively keep them in check. Visibility tools foster accountability by making it easier for teams to track whether they are meeting usage and spending goals. For example, if they overspend during a particular quarter, they can quickly identify which services or resources are responsible.

Enterprises rely on multiple tools and methods to track and manage costs:

• Tagging, or applying special labels to various accounts, services and databases, is one of the primary ways that companies obtain a comprehensive view of their cloud spending.
  
  
• Enterprises might apply resource tags to specific items within a cloud account, offering granular detail into each team’s resource usage. Tagged resources might include Kubernetes clusters, gateways, application services or storage buckets.
  
  
• Account tags, meanwhile, show which resources are associated with which account, providing a higher-level look at company-wide cloud usage. Account tags might indicate details such as owner, region, cost center, security level or business unit.
  
  
• Tag mapping refers to the process of linking tags that share certain attributes to unveil hidden usage patterns and trends. For example, all production-related items might use a shared tag so that organizations can map them to a single dashboard.
  
  
• Cost categories sort resources by cost, helping organizations prioritize optimization for pricier services or shift investments toward more affordable resources.
  
  
• Organizations can implement rigorous monitoring programs, which allow them to regularly reflect on their cloud expenses and reevaluate inefficient processes.

After organizations obtain a comprehensive view of their resource usage, they can turn to cost management tools and pricing models to balance savings with performance and scalability.

In the context of CFM, the rightsizing process involves adjusting the scale and type of fixed cloud resources (such as storage space, compute limits, database instances or network components) available to stakeholders to match their workloads. By analyzing historical usage trends, teams can limit the risk of unintentionally over- or under-provisioning resources.

Enterprises can use automation to scale the size and number of available resources in real time using metrics that analyze demand and workload patterns. This helps ensure that teams or clients are not scrambling for resources while completing projects. At the same time, it reduces wasteful spending by cutting resources that are no longer in use.

Some cloud providers offer savings plans that allow companies to purchase a set amount of resource usage over a set period (often 1–3 years) at a significant discount. If thoughtfully implemented, savings plans can reduce overall cloud expenses by limiting reliance on more costly on-demand usage.

Reserved instances are like savings plans, except they apply to instances (server configurations) rather than hourly compute usage. This approach is less flexible than savings plans, so it’s ideal for predictable, stable usage scenarios.

Companies can claim a cloud provider’s leftover capacity at a steep discount through spot instances. This option carries risk because providers might redirect compute to other clients with little notice, causing unexpected service disruptions. As a result, this approach is best for fault-tolerant tasks such as batch processing and big data analytics.

Companies can improve cost efficiency by scaling resources in accordance with a particular time, date or location. For example, an e-commerce site might ramp up server capacity in the United States during a local holiday to accommodate an influx of customer traffic.

Holding teams accountable for their own cloud costs can motivate them to work more efficiently and prioritize sustainability. A showback strategy sends teams bills with a detailed account of the cloud resources that they used. Chargeback goes a step further by requiring teams to pay these bills with their own funds.

Overly complex cloud infrastructure can introduce inefficiencies and redundancies, making it more difficult to carry out accurate capacity planning. For example, decommissioned programs can siphon resources away from current projects long after they have been rendered inactive.

To reduce waste, enterprises can use anomaly detection tools, often powered by machine learning, to help identify signs of waste.

Many organizations are not only concerned with cloud spending in the moment—they also want to prepare for future scenarios. Forecasting entails studying previous months’ cloud usage, isolating factors that contribute to or detract from business goals and optimizing budget and infrastructure to accommodate future scenarios.

To account for uncertainties such as market conditions or industry-wide disruptions, enterprises might simulate multiple outcomes and coordinate contingency plans for each. Predictive models might also anticipate the cost of net new workloads, so organizations can prepare for upcoming service changes triggered by new cloud investments.

Effective CFM frameworks often incorporate comprehensive guardrails and enforcement strategies so that teams can act independently while maintaining core business objectives. Centralized cloud governance teams often use performance benchmarks, cost controls and compliance reviews to protect departments from runaway costs, security lapses, service disruptions and other risks.

For example, a company might place access restrictions on a resource-intensive service so that only a select few developers can use it. Governance teams might also provide trainings to teach colleagues about cloud computing best practices and efficient decision-making strategies.

CFM strategies often promote transparency and collaboration between teams, helping ensure that no department becomes siloed from the wider organization. Cross-communication helps ensure that every team understands cost savings and efficiency goals. Instead of exclusively taking orders from the IT department, teams meaningfully contribute to the overall strategy, bringing their perspective and data to the table.

To facilitate these collaborations, enterprises might create a central team, made up of representatives from each department, to share ideas and build cohesive strategies. Engineering teams can offer technical expertise, finance teams sophisticated budgeting and cost-cutting strategies and product teams high-level insight into product direction and prioritization.

Most major cloud providers, including Amazon Web Services (AWS), Microsoft Azure, IBM Cloud Services and Google Cloud Platform, provide built-in tools to help companies track and manage their cloud costs. AWS holds about 30% of the market share for the worldwide cloud infrastructure market, followed by Microsoft (20%) and Google (12%), according to HG Insights.

Companies can also choose from multiple non-native cloud financial management tools, which are designed to help enterprises maintain cost visibility and optimize performance across multiple cloud environments. Common options include Flexera, Datadog, CloudZero and IBM Cloudability.

While CFM offers a more comprehensive approach compared to traditional cloud cost management (which focuses mainly on reducing costs), the framework also introduces a new level of complexity that risks overwhelming enterprises if not implemented with care. Common challenges include:

Companies often use several services and storage solutions to operate and maintain their IT infrastructure. While this approach enables organizations to harness the strengths of each service, multicloud environments can also introduce transparency challenges and compatibility issues.

For example, usage data from a particular cloud service might not easily transfer to the company’s central monitoring database. Or, teams might come to rely on their own cost tracking tools, obscuring the organization’s view of overall cloud spending.

Enterprises can address this problem through strong governance and compliance policies, giving teams clear guidelines for how they can integrate new products and services into existing cloud infrastructure.

Teams often have competing interests that can interrupt the organization’s larger CFM strategy. For example, the IT department might aim to reduce company-wide cloud usage, even as the development team scales up its usage to prepare for the release of a new product.

Financial accountability policies, which sometimes require business units to pay for the IT services they use, can also cause friction if teams feel they are being unfairly charged. To foster team alignment, enterprises can create shared incentives and business goals that require input from multiple stakeholders.

Enterprises rely on forecasting to outline future budget scenarios, but hidden costs, inconsistent workloads and unexpected usage spikes can scramble their calculations. Enterprises often combat this volatility by diversifying their pricing models, which helps ensure that they are not overly reliant on a single spending strategy.

Advanced predictive analytics tools, which use statistical modeling and machine learning to analyze an enterprise’s historical data, can also deliver more variable, accurate budget estimates compared to traditional approaches.

Because multi-layered usage bills can be difficult to interpret, enterprises might be unable to identify the main drivers contributing to higher costs. This also cuts into their ability to track spending in real time, meaning they might learn about usage spikes only after an incident has already occurred.

To address these issues, enterprises can use resource tagging to track items as they move throughout the organization. Cost intelligence platforms can help optimize spending by identifying inefficiencies and suggesting behavioral changes to address them. Automated scalability systems can immediately respond to resource needs, and robust data analytics platforms can turn abstract statistics into visuals and dashboards that enable teams to get a better sense of their usage over time.

Despite adding complexity to enterprise operations, CFM features numerous benefits, including improved efficiency, scalability, adaptability and more.