The dynamic nature of Kubernetes workloads and the complexity of rightsizing resources make efficiently managing Kubernetes deployments to cut waste and cloud costs a huge challenge. Constantly tuning Kubernetes resources to drive down cloud costs takes time away from driving business value.
According to a CNCF Survey:
- 49% say Kubernetes has driven cloud spend up
- 70% say workload over provisioning is the source of overspending
Why it’s so Hard to Combat K8s Costs
Overprovisioned workloads: Default resource requests and resource limits are typically set with a buffer across Kubernetes clusters to safeguard performance, leading to wasted compute resources.
Manual resource management: Tuning CPU and memory requests and limits manually across large Kubernetes infrastructure estates is incredibly time consuming and error-prone.
Ensuring Reliability: Rightsizing resources often comes at the expense of performance. Using common tools like the horizontal pod autoscaler make it difficult to cut costs while maintaining application performance.
Unrestricted Cloud Spending: Ungated growth of the Kubernetes cluster without continuous optimization leads to high compute costs across different cloud providers.
The Cost Optimization Challenges
The simple reality is that managing Kubernetes resources involves a lot of technical complexity. It also requires constant attention as workloads needs are highly dynamic – the double edged sword of Kubernetes’ flexibility. As application usage ebbs and flows, resource allocation must be continuously adjusted accordingly.
That constant attention to resource usage across Kubernetes infrastructure translates to hours of manual toil for a single workload at every point in time. This amounts to two full time engineers worth of time required to manually deploy 500 workloads. For any organization beyond Day 1 of their Kubernetes journey, it’s simply untenable for humans to accomplish this on their own.
Figuring out where to start your cost optimization journey brings a whole new set of questions:
- Where are the majority of my compute costs?
- Should I be using spot instances?
- Do I need any reserved instances?
- Do applications have the right amount of resources
- How will you ensure security?
- How will you control costs while still ensuring application performance
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