If you’re not worried about the cost of your Kubernetes cluster, you might get exactly the speed you need for your POC. Once production is up and running and Kubernetes integration increases, it will be critical to implement a number of… recommended approach in FinOps to keep costs under control. We present you the ultimate Kubernetes FinOps checklist to keep your Kubernetes costs under control.

There’s been a lot of talk about FinOps lately. This is undoubtedly no stranger to the current economic climate. At the same time, the adoption of Kubernetes is more popular than ever. This is not just another article about what both mean and why they are essential in today’s world. This article focuses on applying FinOps practices in a scalable environment such as Kubernetes.

Setting up a Kubernetes cluster is relatively easy with managed services like Google Kubernetes Engine. With quality tools and the right DevOps practices, deploying containers in such a cluster becomes easier. However, doing this in a way that is consistent with the FinOps philosophy can be more challenging.

The FinOps Foundation defines the following three phases. Each phase flows seamlessly into the next, ad infinitum:

• Inform – Visibility and attribution
• Optimize – Usage rate and quotas
• To use – Continuous activities and improvements

Let’s look at how these are applied in a Kubernetes environment. Specifically, with a cloud-managed service that offers unimaginable scalability.

Inform

You want to gain visibility into your Kubernetes consumption and resulting expenses. Because Kubernetes’ approach is based on it namespaceis built for Multi-client capability, multiple teams or departments deploy their applications within a typical cluster. Therefore, you cannot simply assign all cluster costs to just one application, team, or department. Unless you are a SaaS company, you host your solution for a single customer.

Pro Tip: Are you working with Google Kubernetes Engine? Then GKE Cost Allocation is a good place to start.

Go a level deeper, to the application level, and first pay attention to how efficiently resources are being used. When I text you Deployment YAMLs There are many aspects to consider that can influence the use of these sources. Many of these things can affect the cost.

It is important to ensure that your consumption can be attributed to a specific workload, team or project at any time. Kubernetes is perfect for this with its label-based approach. Additionally, if you use GKE, your nodes can be tagged for additional granularity.

Optimize

With this arsenal of information immediately available to all teams involved, it’s time to start optimizing. Take a look at all the available data and put on your Kubernetes expert hat (if you don’t have one, please contact us). These are some standard optimizations:

• No CPU, GPU or memory quotas for you namespace? A misconfiguration can skyrocket your resource consumption. Don’t you define pod CPU requirements, just their limits? Kubernetes copies the limit you specified and uses it as the requested value. Now each microservice consumes the maximum of the resources in your cluster, regardless of the actual load.
• Are you hosting a memory-intensive stack with relatively low CPU consumption on nodes and a standard CPU-to-memory ratio? A large portion of CPUs simply remain idle most of the time. This gets even worse when your cluster needs to scale (more). node add) because you have reached the memory limits. In effect you are adding unused CPUs.
• How do you configure scaling of applications and infrastructure? In a Kubernetes environment, many scaling parameters need to be configured, both at the application level (HPA, VPA) and at the infrastructure level (cluster autoscaling). If you do not use these parameters to deploy autoscaling, you must make educated guesses about allocating memory and CPU resources. If you later discover that your estimate was incorrect, you should re-examine and refine it. Why not let this happen automatically and within preset limits? If you incorrectly configure the fixed number of pods per deployment or node in the node, resources will remain idle, waiting for workloads that never arrive.

Computing resources? Wasted. Unnecessary costs? Developed.

Pro Tip: Any major cloud provider should be able to provide you with these insights, like GKE does with cost-related optimization metrics.

To use

Once you have a complete overview of your Kubernetes costs, you’ll know exactly what each application costs. You have optimized your environment for perfect scaling. Now you can continually assess the business value of each individual application deployed in your clustered fleet based on their true cost, rather than the cost of poor cluster and application management.

The ultimate checklist for Kubernetes FinOps

• Inform: Use all Kubernetes native options to assign costs to teams (NamespacesKeywords…)
• Inform: Make sure you review all data you receive from your cloud provider and that relevant team members have access to the data and are trained to interpret it
• Optimize: Make sure your cluster, nodes, and deployments are sized correctly
• Optimize: Use different nodes with additional features tailored to the workload they host (stains And Tolerances can help with that)
• To use: Cost optimization is not a one-time thing. Continue to evaluate costs and compare them to the value applications provide

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