By Pini Ben-Nahum
VP of R&D

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Developers and DevOps teams working in Kubernetes tend to focus primarily on performance and pay little attention to the cost side of things. When workloads are running smoothly and meeting SLAs, budget considerations often take a backseat until some external force (normally in the form of the finance team) demands that it’s optimized. 

However, the reality is that ignoring cost until finance steps in only leads to inefficiency and wasted resources, and eventually, quite a lot of work on cost optimization. This cycle, where costs are forgotten and then aggressively optimized under pressure, drains considerable time and energy that could be better spent on other strategic initiatives.

In this article, we’ll explore why this reactive approach is detrimental to your environment, team, and budget, and discuss how adopting continuous optimization can help eliminate these inefficiencies in your Kubernetes environment.

The Problem: Performance Over Cost, Until It’s Too Late

It’s easy to see why performance takes precedence. Developers and DevOps teams work tirelessly to ensure systems run at optimal speeds, handling increased traffic and workloads without issues. Kubernetes inherently abstracts infrastructure, which aligns perfectly with the priorities of its practitioners. However, the problem arises when there’s actually a need to focus on cost optimization. It’s common for months to go by without a single thought given to cost, as long as the application is stable and performance is high.

Then, when the quarterly review arrives, the finance team highlights the rising cloud costs, and suddenly, cost optimization becomes a pressing issue. Then, teams dive into these “optimization blitzes,” scrambling to optimize costs and resources, which takes time, effort, and attention away from more pressing technical challenges. But once the project is complete, cost optimization is often forgotten again, until the next surprise cost spike.

Why This Is a Bad Practice

This “reactive” approach to cost optimization has two main issues:

1. Resource Wastage: During the periods when cost is ignored, resources are often overprovisioned, left idle, and/or not allocated efficiently. This results in unnecessary cloud expenses – money that could have been saved if cost monitoring had been integrated into the DevOps process from the start.
2. Time-Consuming Optimization Efforts: When the inevitable optimization efforts are initiated, they often require significant resources to execute. Teams have to pull in people, tools, and attention from other projects, slowing down overall productivity. Plus, these optimization efforts often focus on short-term fixes rather than a continuous, proactive strategy.

In short, this cycle wastes time, money, and resources.

A Better Approach: Continuous Cost Optimization

Optimizing Kubernetes costs requires small and ongoing tweaks to keep all the resources aligned with real-time needs. That’s why manual interventions often lead to inefficiencies, as they rely on human expertise and attention to adjust the system for optimal performance. 

So, rather than treating cost optimization as a periodic, reluctantly executed effort, it’s time to integrate continuous cost management into the daily workflow. By making cost optimization a routine process, you can prevent the cyclical waste of resources and avoid dedicating so much of your team’s time and efforts to these “optimization blitzes”.

Here’s how continuous cost optimization works in a Kubernetes environment:

1. Ongoing Monitoring and Visibility: Tools like Kubernetes’ Vertical Pod Autoscaler (VPA) or AWS Cost Explorer allow teams to monitor both performance and cost simultaneously. Obtaining visiblity into your workloads is a crucial first step for anyone who wishes to gain control over their cloud budget and cost optimization. 
2. Automation of Cost Optimization: Leveraging automation tools to continuously adjust resources based on real-time demand is the only effective way to optimize your environment 24/7. Without automation, it’s almost impossible to keep up with fluctuating demands, often leading to overprovisioning or underutilization of resources. Tools like VPA and HPA are designed to automate application scaling by monitoring resource needs and adjusting pod allocations in real-time. On the infrastructure side, Karpenter scales nodes, ensuring your infrastructure scales efficiently, and matching the exact needs of your application without over-provisioning.
3. Integrated Performance and Cost Strategies: Combining performance monitoring with cost tracking enables teams to optimize both aspects simultaneously. Rather than focusing on one at a time, this approach ensures a holistic view of the environment, aligning both cost and performance goals.

This process may feel tedious at first, as it requires adjusting the way your team approaches workloads and cost optimization. Over time, however, it will become much smoother. Once you start building systems and processes with cost in mind, the continual management of resources will become a seamless part of your workflow.

Automating Cost Optimization with Zesty

If you’re looking to switch to an automated and proactive Kubernetes cost optimization strategy, Zesty offers a comprehensive platform that can help you do just that. Powered by the Multi-Dimensional Automation (MDA) approach, Zesty’s platform combines multiple automation technologies to ensure that your Kubernetes infrastructure is continuously aligned with the real-time requirements of your system. 

Key Features of Zesty’s Automation Stack:

• HiberScale Technology: Enables rapid node deployment and reactivation within 30 seconds, reducing the need for overprovisioning and minimizing idle resources. This capability supports our Headroom Reduction strategy, allowing for tighter resource management without compromising performance.
• Pod Rightsizing: Automatically adjusts CPU and memory allocations for pods based on real-time usage, ensuring that resources are neither underutilized nor overprovisioned.
• Persistent Volume (PV) Autoscaling: Dynamically scales storage volumes to match application requirements, preventing both shortages and excess capacity.

Through these automations, users can continuously align their configuration of their Kubernetes environment with real-time cost and performance needs without any manual intervention. The platform’s rapid scaling abilities enable it to handle traffic spikes and ensure stability and performance, while significantly cutting compute costs.

Embracing an Automated Future 

The old cycle of ignoring cost and only addressing it when finance steps in is inefficient and unsustainable. Continuous cost optimization is not just a good practice—it’s essential for long-term cloud efficiency. By integrating cost management into your DevOps process from the outset and automating adjustments as much as possible, you can reduce waste, save time, and avoid the hassle of reactive optimization efforts. The key is to make cost optimization an ongoing, seamless part of the Kubernetes lifecycle.

By adopting continuous cost optimization, you can future-proof your Kubernetes environment, making cost management a seamless part of your DevOps routine—rather than a reactive, disruptive task.