Cost management gets complicated fast in Kubernetes, and more businesses will face this problem soon. According to Gartner, 75% of companies will be running containerized applications in production by 2022.
If you use Kubernetes on AWS, you’re probably implementing best practices to reduce your bill already. To maximize your cloud cost savings, though, you need to understand the specific challenges Kubernetes poses in cost management and optimization. Read this article to find out what they are and how to handle them.
Before containerization, allocating resources and costs was way easier. You just had to tag resources to a particular project or a team. This was enough for FinOps to determine your typical cost structure and control your budget better. Calculating the total project cost was easier once you mapped the vendor tags and identified the team that owns the project.
Naturally, in this scenario, you’d also be run the risk of overprovisioning your resources. Developers might order more resources than they need to make sure applications run without interruption. As Kubernetes and other containerization tools become more widespread, the traditional process of allocating and reporting on costs doesn’t work anymore. Figuring out Kubernetes cost estimation, allocation and reporting isn’t easy.
If you still can’t make sense of your team’s expenses in detail, don’t worry. You’re not the only one out there who struggles to keep costs at bay. To improve your cost control, start by exploring these cost challenges.
Calculating the cost of a single container isn’t hard on its own, but it requires infrastructure and the time to do it.
Kubernetes clusters are shared services run by multiple teams, holding numerous containers and containing various apps. Once a container is deployed, you use some of the cluster’s resources and pay for every server instance that is part of this cluster.
Now imagine that you have three teams working on 10 unique applications. Knowing which application uses your cluster resources is next to impossible because each of these projects uses multiple containers. You have no idea which part a given team is using and how much of it is being used in a particular project.
In short, it’s not clear how many resources an individual container uses from one specific server. This makes calculating and allocating costs a tad more difficult.
Your company contains multiple cost centers, and not all development costs come from the DevOps budget. Some applications might be created by one of your product teams, an R&D team or another team in your IT department for a shadow IT project.
The size and structure of your organization are key here. If your company offers multiple digital services, each of which has its teams and budgets, tracking the costs of the cloud services gets complicated. When multiple teams use the same cluster, defining which team or project is responsible for which part of the final bill becomes a challenge.
It gets even harder to track once you consider multicloud. A Gartner survey of public cloud users shows that today 81% of respondents are working with two or more providers. According to ICD, 90% of enterprises will rely on multiple clouds or a mix of on-prem, private, hybrid and public clouds by 2022.
So, soon you might be running your Kubernetes clusters across multiple clouds and your containers will be using different nodes.
Your applications can be scattered across different clouds such as AWS, Google Cloud Platform, Azure or Digital Ocean. Each of them might host just a tiny part of your overall workload, which further complicates tracking nodes and clusters.
To make the most of Kubernetes, most teams use built-in Kubernetes autoscaling mechanisms. The tighter you configure them, the less waste and lower costs of running your clusters.
While Vertical Pod Autoscaler (VPA) automatically adjusts requests and limits configuration to lower overhead, Horizontal Pod Autoscaler (HPA) focuses on scaling out to reach the optimum amount of CPU or RAM allocated to an existing instance.
These scaling mechanisms affect cost calculation, however. VPA constantly changes the number of requests on a container, expanding and shrinking its resource allocation. HPA, on the other hand, changes the number of containers dynamically.
For example, imagine three web server containers running during the night. During peak hours, HPA scales from three to 50 containers. Then, it scales down during lunch and then back up. In the evening it settles at a low level.
This means that the number of containers and their sizes are extremely dynamic, making the process of calculating and forecasting costs much more difficult.
With containers, you can reschedule workloads across a region, zone or instance type. A container’s lifespan is just one day. It’s a small glimpse in time when compared to how long a virtual machine can last. More and more people run functions and cron jobs on Kubernetes. The lifetimes of these are from seconds to minutes.
The dynamic nature of the containerized environment adds another layer of complexity to the mix. Your cost management system needs to be able to handle that.
To avoid falling into one of the traps outlined above, you need a solid cost analytics process based on reliable data sources. Here’s an example to show you what it could look like:
- Find a cost visibility tool to track costs in detail — for example, at the microservice level.
- Once you have cost visibility in place, you can set precise budgets and monitor elements such as traffic costs to understand them better.
- Next, allocate your costs by namespace, pod, deployment and label.
- Analyze the pricing information to predict how much you’ll have to pay next month.
- Keep monitoring costs against your estimates and pinpoint cost or usage anomalies to analyze them further. Currently, most companies solve this problem manually, but what if you could automate this entire process?
Allocating resources, calculating costs and analyzing Kubernetes pricing information shouldn’t be as challenging as it is today. Syncing up the cost and resource allocation is the way to go.
What are the must-have features to look for in an automation tool?
- Advanced cloud bill analysis and cost visibility feature with the ability to analyze costs down to individual microservices and get universal metrics for any cloud provider.
- Automated instance selection and rightsizing.
- Use of spot instances for up to 90% cost savings.
- Forecasting expenses for projects, clusters, namespaces and deployments. ### Automated Cost Management Betting on manual strategies for controlling your Kubernetes cloud costs is risky. They’re usually time-consuming, error-prone and difficult to maintain.
Deploying an automated cost management solution saves you lots of headaches and helps you focus on what matters most to your business: delivering quality service to customers.
We built CAST AI to do just that. Book a demo and give it a spin to see your Kubernetes costs go down.