How to Cut Your AWS Kubernetes Costs Without Sacrificing Performance

There are some critical considerations for all the power cloud computing gives us. You’ll want to keep those bills under control, but it may seem like trying to catch a runaway shopping cart.

Kubernetes is powerful but incredibly resource-hungry if it isn’t optimized correctly. In fact, according to a report from CNCF, half of organizations spend more with Kubernetes, mostly due to overprovisioning.

So, how do we keep it from devouring our entire budget?

Let’s explore the top eight Kubernetes creative strategies for cost optimization. These are some of the strongest cost optimization strategies you can consider without compromising on the fantastic performance you’ve come to rely on.

Make Your Kubernetes Cluster Work Smarter AND Harder

Kubernetes was created to help you work smarter and facilitate a better environment for those who work hard.

Through dynamic cluster auto-scaling and fine-tuning your resource limits, you can teach Kubernetes the best ways to help you so that you save on costs and only use the resources that you need at all times.

1. Dynamic Autoscaling

One of Kubernetes’ strongest values is its ability to auto-scale clusters. You can automatically adjust the size of your cluster based on real-time demand.

There are two primary autoscaling techniques that you should be aware of:

• Cluster autoscaler: Automatically adjusts the number of nodes in a Kubernetes cluster to meet the demand for resources.
• Pod autoscaler: Dynamically changes the number of pods in a deployment or replica set based on CPU utilization or other metrics.

You won’t mistakenly over-provision resources using these features, which will save you money and cut unnecessary costs.

Kubernetes already has a built-in function that lets you achieve actual auto-scaling efficiency: the Horizontal Pod Autoscaler. It updates workload resources to match demand, and over time, you and K8s will learn the peak periods of your traffic and will be able to materialize cost savings along with it.

2. Fine-Tuning Your Pod Resource Request and Limits

Kubernetes lets you define resource requests and limits for your pods. These are guidelines for how much CPU and memory each pod needs to function smoothly.

You can manually fine-tune these settings to prevent resource waste and ensure your deployments are “lean and mean machines,” running efficiently without exceeding their actual requirements.

If you’re interested in automating these actions, there are also a number of 3rd party tools that allow for continuous rightsizing. They can help optimize your Kubernetes on either pod or instance level.

The Best Way to Balance Cost and Availability

If the only goal is cost savings, purchasing the cheapest deal will be the go-to strategy. However, you’ll want to aim for a cost-effective and affordable environment to confirm that you balance your resource usage and match it with your actual demand.

Spot instances and reserved instances are two ways to adjust your bills accordingly without relying only on On-Demand instances.

3. Use Spot Instances Strategically

AWS Spot Instances offer a compelling cost-saving opportunity. They are spare compute capacity in the AWS cloud, available at significantly lower prices than On-Demand Instances. They’re like the cloud’s clearance section but have some drawbacks.

These instance types can be interrupted by AWS when needed to direct them to more resource-hungry demand workloads.

How can you use them efficiently? Spot instances are ideal for stateless workloads that can tolerate short interruptions. Remember that these resources will be returned to you when the demand surge ends.

Using a spot instance in non-critical environments can save you as much as 90% on your next bill.

With clever interruption handling strategies (like quickly draining pods before termination), you can take advantage of Spot Instances’ cost advantage while minimizing disruption to your apps.

4. Predictable Workloads? Reserve Some Instances

AWS Reserved Instances offer a cost-effective alternative for apps with predictable resource demands. They provide significant upfront discounts compared to On-Demand Instances but lock you into a specific instance type and region for a set period.

Having pre-paid computing resources at a discounted rate carries no noteworthy risk if you can accurately predict periods of demand and know precisely what upper limit you need. As long as you stay within the boundaries, reserved instances will provide significant cost savings in the short and long run.

Implement Your Savings at Scale

Kubernetes does a great job scaling resources to match their demand flows for different workloads. In addition to auto-scaling, you can use Kubernetes to its fullest potential by scaling CPU and memory within a node to match periods of high and low demand.

Scheduled scaling takes this a step further and can even shut down resources completely in short periods that necessitate a brief idle time.

5. Mastering Your Resource Scaling

Earlier on, we covered the built-in auto-scale feature for your K8s environment. Scaling isn’t just about adding or removing nodes (horizontal scaling). Consider implementing intelligent autoscaling solutions that cover all your bases.

You can implement strategies that include horizontal scaling, vertical scaling (scaling CPU and memory within a node), and even scheduled scaling.

Scheduled scaling lets you proactively adjust resources based on predictable usage patterns, guaranteeing your services are always the right size for the job.

With scheduled scaling, you’ll need to start with the most predictable resource usage patterns to ensure that Kubernetes is auto-scaling properly between these periods.

Translate Your Cost Analytics into Insights

What gets measured gets done. This is especially true for tracking costs and seeing where most expenses are allocated when deploying K8s at scale. The Kubernetes environment provides you with cost tracking for resource allocation, but this may not paint the overall picture.

Gain more insights with powerful cost-tracking tools, implement the data-driven insights to achieve quick wins in the expense nodes to start, and deploy this at scale to optimize true cost performance.

6. Cost Tracking Using The Right Tools

Kubernetes offers some built-in basic cost tracking, which includes

• Resource Consumption: You can track how much CPU, memory, storage, and network resources your Kubernetes deployments consume. This data is provided through Kubernetes or your AWS cloud provider account.
• Monitoring Cloud Billing: You monitor your cloud provider’s billing statements to see how much you’re being charged for the resources your Kubernetes cluster consumes.

Basic cost tracking is a great place to start, but it provides limited visibility. Your team may not be able to garner enough insights. You can tell HOW much you are spending, but it may be challenging to track WHY.

Advanced cost-saving strategies such as AWS Cost Explorer are a great place to start, allowing you to refine cost-saving strategies.

To improve Kubernetes performance at scale, you’ll want to try out the new auto-pilot feature for Intel® Granulate™ Capacity Optimization which enables continuous and automatic improvements. This groundbreaking feature allows Kubernetes users to opt into autonomous optimization, which adapts resource requests and HPA settings in real-time to reduce CPU and memory overhead.

Push Boundaries and Build a Powerful Culture

Culture isn’t something you can teach, but it is definitely something your brand can develop over time. Supporting your team members in making decisions that optimize costs while holding them accountable for cloud spending is a great way to reward behaviors that benefit the company.

Encourage your team to stay up-to-date on the latest techniques and cost-optimization strategies for Kubernetes while monitoring their resource usage.

7. Train Your Team Around Cost Awareness

Optimizing cloud costs is a team sport. You can promote a culture of cost awareness across all teams involved in your cloud infrastructure.

When everyone, from engineers to project managers, understands their decisions’ impact on cloud spending, your organization can collectively achieve significant cost optimization.

Organize workshops or training sessions focused on the core principles of cost optimization and best practices in Kubernetes. Cover topics like resource requests and limits, autoscaling strategies, spot instances, and reserved instances.

8. Combine Culture With Innovation

Don’t be afraid to experiment with innovative tools and practices that challenge the status quo. When your brand’s culture is on-side with cost-conscious decisions, you can make this easier to track through intuitive tools.

Beginning with a tool such as AWS Cost Explorer can expand the visibility within your metrics. Breaking down costs by namespace, pod, workload, and environment will allow teams across all departments to identify areas for improvement.

Create dashboards and reports that display key cost metrics and trends. Share these dashboards with relevant teams during meetings or through internal communication channels. This ongoing exposure to cost data keeps cost-consciousness top-of-mind.

Establish cost targets for different teams or projects. These targets should be realistic but also encourage cost efficiency. Regularly track progress towards these targets and celebrate achievements.

Sustainable Savings for Your AWS K8s Journey

Getting a handle on reducing IT costs can be difficult, especially when you’re trying to pinpoint precisely what resources are tying up your bill. Cost optimization is paramount to cost-savings, as reducing your expenses shouldn’t also cause downtime in your services or reduce the user experience.

With auto-scaling, rightsizing resources, creating a solid culture around cost-optimization, and further refining these strategies, you can decrease your costs while maintaining your current level of performance and reinvesting the difference into your competitive advantages.

But how can you optimize costs without relocating your current staff and resources so that it costs you less to track and adjust than you’re saving?

Intel Granulate Capacity Optimization is a powerful solution that can reduce costs by up to 45% and requires 0 code changes and works with minimal supervision.