Understanding and implementing cost optimization capabilities across all your cloud environments.
A recent analysis of over 2100 organizations (2025 Kubernetes Cost Benchmark Report) has found Kubernetes clusters drowning in waste, with actual utilization averaging a meager 10% for CPUs and 23% for memory. Forget efficiency, it’s a budget blackhole. Unfortunately, these stats remain stubbornly consistent with last year’s report, proving that organizations are still struggling to manage Kubernetes clusters effectively despite growing maturity and awareness of optimization strategies.
➡️ Kubernetes (often abbreviated as K8s) is an open-source container orchestration platform that helps manage, scale, and deploy containerized applications efficiently. Originally developed by Google, K8s is now maintained by the Cloud Native Computing Foundation (CNCF).
Historically, with on-prem infrastructure and resources, developers were primarily concerned with performance and availability rather than efficiency. Organizations had to ensure they always had enough capacity to handle peak demand. As a result, developers didn’t need to predict actual resource requirements with precision because the hardware was already paid for, and the worst-case scenario was running out of capacity, not overspending.
Self-service provisioning and a lack of accountability for infrastructure costs make it easier for DevOps to overprovision resources without oversight. In addition, the fear of throttling, pod eviction, and downtime impedes any rightsizing efforts.
Cloud’s on-demand resource availability and pay-as-you-go pricing changed that equation, as overprovisioning means spiralling costs. Yet, the overprovisioning mindset has persisted. Rightsizing isn’t always a priority, but there are other barriers to it as well. Self-service provisioning and a lack of accountability for infrastructure costs make it easier for DevOps to overprovision resources without oversight. In addition, the fear of throttling, pod eviction, and downtime impedes any rightsizing efforts.
Another study shows that 59% of containers have no CPU limits set, as without proper usage information and accurate resource prediction, setting up requests and limits can lead to performance downgrades or Pod eviction. So, there is a tradeoff between efficiency and performance that’s impossible to balance manually and without the right insights.
Optimizing Kubernetes when the scale exceeds hundreds, even thousands, of nodes is challenging when done manually, and the stats show it. The 2100 organizations analyzed for the above-mentioned report were not using any kind of Kubernetes management tool, which would’ve provided the intelligent insights and alerts needed to curb waste and optimize resource allocation and utilization.
Top cloud service providers (CSPs) have their own managed Kubernetes services like EKS by AWS, Azure’s AKS, and GCP’s GKE. However, businesses may sometimes avoid using those, since they can tie you into one provider. You’ll have to use more than one service if you have clusters across multiple clouds. Managing more than one Kubernetes service is hard because all cloud providers have different configurations for networking, scaling, and security and different CLI commands and syntaxes. Managing multi-cloud clusters is even harder.
As such, you need a third-party managed Kubernetes service that can provide a holistic view and optimization capabilities across all your environments. Below are some proven Kubernetes cost optimization strategies and how the emma cloud management platform can help you implement them:
Rightsizing workloads in Kubernetes requires real-time usage data for demand-based, instant autoscaling. emma’s intuitive dashboard displays real-time CPU and memory utilization per node to help you adjust your autoscaling policies and requests and limits accordingly. In addition, it provides actionable recommendations for optimizing resource allocation based on current and historical usage trends and ML-based predictive analytics. These features help you ensure that your application requirements are met without the risk of over- or under-provisioning.
emma’s continuous monitoring capabilities ensure that teams are notified when resources behave unexpectedly. It also identifies idle and underutilized nodes and automatically decommissions unnecessary nodes to avoid cost drain. For instance, uninitiated nodes that have been provisioned but not utilized continue consuming resources even though they don’t run any pods. The emma platform identifies such nodes and, based on your defined policies, alerts your team or automatically decommissions them.
Clusters running on spot instances can save between 59% and 77% compared to on-demand instances. However, spot instances are unpredictable, and you need to continuously find the best available prices across all your cloud environments. Manually, this can be inefficient and time-consuming, but emma does the job for you.
A major benefit of using spot instances with emma is that you can add markups and price limits for automatically purchasing and deploying spot instances while maintaining complete cost control and efficient resource utilization.
It finds the cheapest spot instances available across regions and cloud providers, allowing you to quickly deploy or migrate to the most suitable option. A major benefit of using spot instances with emma is that you can add markups and price limits for automatically purchasing and deploying spot instances while maintaining complete cost control and efficient resource utilization.
Compute costs vary widely not only across cloud providers but also across different regions of the same cloud platform. Companies can save up to 7x compared to Spot Instance pricing and up to 10x compared to On-Demand pricing by shifting workloads to more cost-effective regions and Availability Zones (AZs). Aggregating all the price data available and shifting workloads seamlessly is the actual challenge.
The emma platform provides real-time cost data for your preferred configurations and allows you to consistently deploy, manage, and expand Kubernetes clusters across multiple clouds and regions, all through a unified control plane.
With this handy calculator, you’re given a real and tangible look at how much cost you can cut with the right tools. Compare savings between using emma and other Cloud Providers, and see for yourself!
Despite how common and extreme Kubernetes overprovisioning is right now, it is entirely avoidable through AI- and ML-powered automation. With AI and automation, rightsizing, autoscaling, spot instances, and cross-region deployments can deliver immediate cost savings. Over time, other benefits and even greater savings emerge because of controlled self-service and reduced need for hands-on optimization, leaving more time for innovation and value generation.
With emma, Kubernetes cost efficiency is no longer guesswork – it’s about implementing data-driven, proactive strategies with complete visibility and control over the benefits and results. Try emma now with a 14-day free trial or request a personalized demo!
Learn more practical strategies for cloud cost optimization and value realization in our latest blog series:
Part 1: Realizing Positive Cloud Value and Business Objectives to Maximize Cloud ROI
Part 2: 10+ Advanced Strategies for Cloud Cost Optimization
Part 3: How emma Powers Cost Optimization & Value Realization
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