Cloud cost management defined
Cloud cost management, also known as cloud spend management, is the process of monitoring, controlling, and optimizing cloud spend across an organization’s cloud environments.
Cloud spend management isn’t just about lowering costs—it’s about aligning spend with business and technical goals. It’s being able to tell if the cost of a cloud project is worth the spend and if cloud resources are deployed in a way that minimizes waste—without compromising performance.
Because answering these questions requires the combined efforts of finance, engineering, DevOps, security, and other stakeholders, cloud cost management is a central goal of financial operations, or FinOps. (FinOps is a framework that unites these multidisciplinary teams to tackle the task of maximizing the business yield from cloud spend.)
Beyond optimizing spend, cloud cost management also involves driving better pricing from cloud vendors and boosting margins through cost optimizations.
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Why cloud cost management matters
Unlike a relatively static on-premises infrastructure and its predictable costs, the cloud is dynamic, with resources being spun up and down rapidly. As a result, cloud costs are constantly evolving and can escalate unpredictably when they’re left unmanaged.
So while annual budgeting and monthly oversight may work in traditional settings, the same becomes useless when a forgotten namespace or an autoscaler racks up thousands of dollars unexpectedly before the month’s end.
But there are other complications waiting in the wings. Many common cloud trends add more complexity to tracking cost and require a tight grip on cloud spend management:
Enterprises operating multi-cloud environments struggle with fragmented visibility and varied pricing models, making correlated insights impossible without unified cost monitoring.
Kubernetes and similar orchestration platforms have abstracted infrastructure layers and autoscalers working at full speed. This architecture makes cost attribution difficult because it requires tools that calculate and visualize costs faster than the cluster changes.
In each of these models, if cloud costs aren’t properly managed or accounted for, enterprises suffer unchecked cloud spend, which has serious business and technical implications, including:
Unexpected cloud bills: Cloud costs add up very quickly due to problems like unmonitored resources, shadow IT, and misconfigurations. The result? Bill shock, with cloud spend far outstripping budgets.
Overprovisioned resources: When engineers lack visibility into cloud costs and cost isn’t a factor in project planning, enterprises can purchase significantly more compute than actually needed. This defeats the cost-saving pay-as-you-go model of the cloud, fueling cloud waste.
Idle services: Cloud resources are easily spun up and easier to forget about, resulting in orphaned resources and environments (e.g., unattached containers, storage buckets, and test environments), which continue to eat into the budget long after they’ve been abandoned. And because these abandoned resources have lower monitoring priority, they also pose security risks.
Cost attribution problems: Mismanaged cloud costs don’t just bloat budgets—they obscure business value. Without granular cost data tied to teams, services, products, or customers, organizations struggle to answer basic questions like, “What does it cost to run X product?” or “What’s the ROI on Y workload?”
Cloud cost management vs. cloud cost optimization
One thing is clear, cloud spend management is critical. But how is it different from cloud cost optimization, and which is more important?
| Definition | What’s involved | |
|---|---|---|
| Cloud cost management | Cloud cost management refers to monitoring, analyzing, and reporting on cloud spend to enable predictability, accountability, and optimization. |
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| Cloud cost optimization | Cloud cost optimization transforms cloud cost visibility and business context into actionable insights that can help you make changes to reduce costs and eliminate cloud waste. |
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Basically, both are essential. Without cloud cost management, effective cloud management and optimization is impossible—you can’t optimize something that’s not on your radar. And cloud spend management without optimization is like pouring effort down the drain—cost visibility means nothing if it isn’t channeled towards eliminating waste.
Who owns cloud cost management?
When it comes to who takes charge of cloud spend management, organizations adopt various models. Let’s take a look at a few:
Model 1: Engineering ownership
In some organizations, cloud costs are owned by centralized platform engineering or DevOps teams who focus on monitoring, governing, and optimizing cloud costs through deep infrastructure expertise.
The upside? Engineers are responsible for the cloud costs of their applications and services they’re building. The downside? There’s a disconnect between the engineering team and the finance team. The finance team is still responsible for budgeting, forecasting, and more, but lacks context and visibility into the drivers of cloud cost.
Model 2: Finance ownership
Cost management may also be led by finance teams who focus on budgeting, procurement, cost analysis, cost allocation, and showback/chargeback.
These teams deploy finance expertise to align spend with business goals. That said, they lack the cloud infrastructure visibility and engineering know-how to turn cost reports into actionable findings for optimization. They can see where they’re spending the most, but they lack the business context to understand if that spend is essential.
Model 3: Shared ownership through FinOps
Organizations are increasingly adopting the FinOps model, a practice that unites stakeholders across finance, engineering, product, and security with centralized cloud cost context, shared tools, and shared goals. Central to the FinOps practice is the idea of a shared ownership model: Engineering teams are responsible for managing and optimizing costs of the services they own, and finance teams for budgeting, procurement, and cost governance.
Many organizations now have dedicated FinOps practitioners who focus on driving cloud cost management and cloud cost optimization through collaboration with finance, engineering, product, and other stakeholders. FinOps practitioners can report to either the finance team or the DevOps team, but either way, their role is cross-functional, and they rely on collaboration with both teams to drive cost optimization results.
The FinOps model provides shared context, powers effective collaboration, drives intelligent cost optimization, and powers granular accountability. One essential component? For an effective shared FinOps model, all stakeholders must have context-rich tooling that empowers them with business, usage, and resource-relationship context to identify and to act on cost optimizations. These tools break costs down to the resource level and show relationships between resources so that teams can see what’s actually driving spend.
Key capabilities of cloud cost management
When it comes to controlling spend and tackling runaway costs, the following elements are the driving forces.
| Key component | What it involves |
|---|---|
| Cloud spend visibility & analysis |
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| Resource utilization monitoring | Understand your actual cloud usage to…
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| Cost allocation & tagging |
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| Forecasting & budgeting | Estimating future costs and setting limits based on historical trends to prevent budget overruns |
| Optimization & recommendations | Finding and implementing cost-saving opportunities, including:
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| Near real-time anomaly detection | Identifying and alerting on anomalies in cloud spend in near–real time so you can respond to spikes in cost before they become massive cost overruns |
| Governance & policy enforcement | Creating and autonomously enforcing cost-related policies and guardrails |
How cloud cost management works in practice
Cloud cost management isn’t about finance teams “Slack-ing” engineering first thing Monday morning over a $10,000 cost spike or engineering teams’ mad dash to find the source of the problem.
Instead, it’s a workflow that arms all stakeholders with 24/7 cost visibility and ensures that mad dash is rarely necessary. Here’s what the cloud cost management workflow looks like:
Step 1- Data collection: Using automated tools, pull usage, billing, and resource data from various cloud providers in real time and unify the data in a single pane of glass.
Step 2 - Normalization & mapping: Use cost management tools that automatically transform data and enrich it with context. This includes tagging resources and associating spend with business units or application services, mapping bills for specific resources across providers for comparison, correlating usage metrics with billing, and grouping spend to see total cloud cost for a product. It also includes building a map of the cloud environment that includes cost data and shows the relationships between resources, so engineers can investigate cost drivers with full context on how everything in their environment connects.
Step 3 - Analysis & visualization: Using dashboards, heatmaps, and cost anomaly alerts, drill down into spend over time, find top cost drivers, detect anomalous spikes across weeks/months, and visualize spend down to the individual units of API calls, serverless invocations, VMs, Kubernetes pods, storage buckets, etc.
Step 4 - Optimization: Employ cost management tools with native automation and recommendation engines. These tools will help identify waste, pinpoint cost-inefficient designs, suggest areas for optimization, and autonomously apply PaC (e.g., deleting resources after 10 hours of disuse). Ideal tools will also map cost optimizations to specific projects and application services so engineering owners can easily see and act on optimizations for the services they own.
Step 5 - Enforcement: Deploy PaC and automation to prevent future cloud waste (for example, by controlling who provisions resources using the principle of least privilege, zero-scaling test environments during non-active hours, and mandating approvals when budgets quotas are close to exhaustion).
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PS: While this is the general cloud cost management workflow, there are a few implementation differences across various compute models. These differences stem from the unique challenges and architectural variations of the compute models.
For example, in traditional VMs, cost per hour gets attributed directly. But cost mapping and attribution is more complex in Kubernetes, EKS, ECS, and other container orchestrators, due to the abstracted infrastructure layer and shared resource usage.
Also, in serverless, massive volumes of functions must be linked to shared backend services like storage and API gateways for effective cost correlation.
Leading cloud cost management tools will be able to handle the nuances of cost mapping and attribution for these different models.
Tools and platforms that support cloud cost management
There’s no shortage of vendors that support cloud cost management. The question is how effective they are. Common categories of cost management tools are:
Cloud provider native tools: These tools, such as AWS Cost Explorer, Microsoft Cost Management, and Google Cloud Billing, are deeply integrated with their respective cloud environments. They excel at providing detailed insights into provider-specific workloads, forming a foundational layer for cost visibility within a single cloud. While excellent for initial tracking within one provider, they offer limited visibility and insights across multi-cloud environments.
Finance-centric cloud cost management: These tools are designed with a strong financial focus, empowering FinOps teams and finance departments. They provide advanced capabilities for variable cost analysis, forecasting, budgeting, and comprehensive financial reporting, including chargeback functionalities. Their strength lies in enabling robust financial governance and accountability, though they may lack the granular, real-time technical context engineers need during the development and operational lifecycle.
Engineering-centric cloud cost management: These tools prioritize surfacing costs directly within the context of infrastructure, applications, and services. They empower development and engineering teams by showing the direct cost-to-value of their deployments, facilitating cost-aware decisions throughout the development and operational lifecycle. These platforms foster a culture of cost-awareness at the technical level.
Emerging cloud operations excellence platforms: This evolving category encompasses providers from adjacent spaces, such as observability, DevOps, and cloud native application protection platforms (CNAPPs) that are extending their capabilities into cloud cost management. These platforms offer significantly richer cloud context and more familiar and efficient workflows for engineers. By correlating cost data with cloud architecture, usage, performance, and security context, they enable a truly holistic view of cloud health and efficiency. This integrated approach leads to more actionable insights, allowing engineers to make informed trade-offs between cost, performance, and security, driving cost savings without impacting business performance.
Of these four major types, emerging cloud operations excellence platforms, including CNAPP platforms designed for engineering users, often offer richer context. How? They tie costs to service ownership and architecture and show engineers exactly how mismanaged resources, cloud costs, and security risks interrelate—e.g., how a misconfiguration spikes costs or how an idle instance increases the attack surface.
Bringing it all together: Context is key
Oftentimes, there’s the misconception that cloud cost management means hacking away at cloud spend no matter what. Or that once a tool offers dashboards or spreadsheets, it’s provided enough context. But in reality, it’s the opposite: Cost can’t live in isolation, and blind optimization cuts costs in a way that ignores potential performance degradation, security risks, and critical business tradeoffs. For example, what if a sudden 10% rise in cloud costs coincides with improved data security or a 30% revenue hike?
Incorporating context into cloud cost management is the only way to detect these metrics and convert them into smart, impactful decisions. Even better? Combining context with shift-left cost management and continuous, real-time visibility. This will allow your business to implement cost optimization from the start, plug sudden spikes quickly, and fix emerging security risks before they impact cost.
That’s exactly what you get with Wiz Cloud Cost Management. Schedule a demo today, and see how Wiz can align your cloud cost management efforts with your business goals, security, and performance needs.
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