How Kubernetes Cost Visibility Is Becoming a FinOps Priority

Most cloud invoices don’t come with a handy cost breakdown. You may know that your cloud costs last month were $500,000, but answering a simple question like which team spent what is often more challenging than you would expect. The difficulty of cost attribution grows exponentially in Kubernetes. With the constant ups and downs of load scaling, cross-cluster resource sharing, and infrastructure costs spread across dozens and dozens of services, it’s no simple task to attribute cost.

Kubernetes cost visibility is rapidly gaining traction within FinOps efforts because organizations have discovered that running a containerized environment is only half the battle. It’s now just as important to understand the cost implications of the workloads being run in that environment. The focus is no longer on spending less money on cloud resources. Instead, companies are realizing the value of pinpointing the origin of these costs, assigning ownership, and understanding how these costs impact engineering work.

Why are Kubernetes costs difficult to track

Historically, cloud cost reporting was built around infrastructure resources such as virtual machines, disks, and databases. Kubernetes changes that. Apps are no longer neatly contained within distinct infrastructure resources. Many teams can share the same Kubernetes cluster, a particular node can service a host of business units through its workloads, and infrastructure resources typically change on a minute-by-minute basis as load scales up or down. Additionally, a workload that lives only for a few minutes may not be easily attributed to anything.

While this flexibility is wonderful on an operational level, it makes the attribution of cloud costs more challenging. Cloud vendors may be able to help an organization understand what a Kubernetes cluster costs, but they may not be as helpful in understanding the costs of a specific app, team, or service in that Kubernetes cluster. Without this visibility, it is difficult to control your cloud costs. Teams often spend too much money without realizing it, and that can be a serious problem.

The rise of workload level cost attribution

One of the more prominent recent trends in Kubernetes cost visibility is cost attribution to the workload. Organizations are realizing they must allocate more granularly. As opposed to allocating by cluster, you can allocate by namespace, application, deployment, or team. With this cost visibility, you get a much better idea of the specific cost of a workload.

For example, a customer-facing app may consume 15% of the cluster resources while an internal analytics workload consumes 40% of the cluster’s resources and costs. Without workload-level cost attribution, both services will be part of the same infrastructure cost. Modern cost visibility platforms are helping organizations start to understand how costs get allocated through the cost metrics of the workload, whether it be CPU, memory, disk, or network traffic, and this helps tie together the cost to the engineering.

This is a giant step toward understanding cost attribution and understanding the cost of the workloads you have.

Chargeback and showback

After understanding the origin of these costs, the next question to ask is who should pay for them. This where terms like chargeback and showback enter the picture. While showback provides visibility without necessarily assigning costs,  chargeback goes a step further by allocating actual costs to business units, departments, or product teams. Previously, these types of models didn’t work as well because the ability to get workload-level cost attribution was limited. As transparency increases, companies are growing more confident in deploying cost transparency systems.

The aim isn’t really to drive down expenses by applying budgetary constraints; it’s to foster better engineering decisions. As long as teams have a clear picture of the price tag of an architecture choice, a request for resources, or a deployment approach, optimizing for cloud spend starts becoming a part of development, not just finance work.

Visibility for resource optimization

Kubernetes deployments are typically over-provisioned. Developers often request more CPU and memory than their workloads really need, and that’s a reasonable choice since over-provisioning is better than an application running too slowly or crashing due to a lack of resources. For a particular service, twice as much memory might not be a big deal, but with hundreds of services and clusters, the cost adds up.

Cost transparency tools let organizations compare resource allocation with actual usage patterns. Platform teams can then provide feedback and recommendations on how to size those services better, schedule them more efficiently, and eliminate wasted capacity. This shift in how cost transparency is being used makes optimization less about cost-cutting and more about waste elimination. The desire is for application teams to optimize application costs, so the applications still perform well and stay reliable, without blowing the budget on over-provisioned applications.

FinOps is moving closer to engineering

While cost transparency was traditionally seen as a finance initiative, engineering teams, platform teams, and cloud architects are now increasingly involved in cost transparency. That makes sense since they’re the ones actually building the environments to run applications. Decisions about what kind of architectures to run, adding new services, deciding to keep historical data around, or overprovisioning for safety all result in higher infrastructure costs. While finance teams can show organizations where those costs are occurring, engineers are the ones who can actually make changes that reduce spending.

This approach helps engineering teams take into account cost optimization, developer experience, and reliability. It also means they can make more informed choices earlier on without the need for finance or operations to come back to tell them that they’re going over the budget.

The cost of Kubernetes deployments has always been hard to understand at a service level. For most organizations using Kubernetes, being able to see workload and service costs is critical to help control cloud spend.