A 30% reduction in Kubernetes costs at scale is a significant achievement, primarily accomplished by shifting from a mindset of “provision for peak” to one of “automate for efficiency.” This involves a three-part strategy: aggressively right-sizing resource requests, implementing intelligent, application-aware autoscaling, and strategically leveraging cheaper, interruptible compute instances.
By combining these tactics, organizations can eliminate waste, reduce their cloud bills, and run a leaner, more cost-effective infrastructure without sacrificing performance.
Tremhost Labs Case Study: A 30% Kubernetes Cost Reduction at Scale
Short Summary
This case study examines how “AfriMart,” a large, pan-African e-commerce platform, reduced its monthly Kubernetes-related cloud spend by 30%, from approximately $85,000 to $60,000. Faced with infrastructure costs that were scaling faster than revenue, the company undertook a rigorous optimization project. The cost savings were achieved not through a single change, but through a systematic, three-pronged strategy:
- Right-Sizing: Using monitoring data to eliminate over-provisioning of CPU and memory requests.
- Autoscaling Optimization: Fine-tuning autoscalers to react more intelligently to real-world demand.
- Spot Instance Integration: Shifting stateless workloads to significantly cheaper, interruptible compute instances.
This report breaks down the methodology and results, providing a reproducible blueprint for other organizations to achieve similar savings.
The Challenge: Uncontrolled Scaling and Waste
AfriMart’s success led to rapid growth in its AWS EKS (Elastic Kubernetes Service) clusters. Their monthly cloud bill, dominated by EC2 instance costs for their Kubernetes nodes, was becoming a major financial concern, especially given the sensitivity to USD-denominated expenses for a company operating across Africa. An internal audit by their platform engineering team, conducted in early 2025, identified three core problems:
- Pervasive Over-provisioning: Developers, wanting to ensure their applications never ran out of resources, consistently requested 2-4x more CPU and memory than the services actually consumed, even at peak load.
- Inefficient Autoscaling: The cluster was slow to scale down after traffic spikes, leading to hours of paying for idle, oversized nodes. Furthermore, pod-level autoscaling was based purely on CPU, which was not the true bottleneck for many of their I/O-bound services.
- Exclusive Use of On-Demand Instances: The entire cluster ran on expensive On-Demand EC2 instances, providing maximum reliability but at the highest possible cost.
The Solution: A Three-Pronged Optimization Strategy
The team implemented a focused, three-month optimization plan.
1. Right-Sizing with Continuous Monitoring
The first step was to establish a ground truth. Using monitoring tools like Prometheus and Grafana, they collected detailed data on the actual CPU and memory usage of every pod in the cluster over a 30-day period.
- Action: They compared the actual usage to the developers’ requests. The data revealed that most applications were using less than 40% of the resources they had requested.
- Implementation: The platform team, armed with this data, worked with developers to adjust the resource
requestsandlimitsin their Kubernetes manifests to more realistic values, typically adding a 25% buffer over observed peak usage. This immediately allowed Kubernetes’ scheduler to pack pods more densely onto fewer nodes.
2. Intelligent, Application-Aware Autoscaling
Next, they addressed the inefficient scaling behavior.
- Action: They replaced the default Kubernetes Horizontal Pod Autoscaler (HPA) settings with custom-metric-based scaling for key services. For their order processing service, which was bottlenecked by a message queue, they configured the HPA to scale based on the SQS queue depth rather than CPU.
- Implementation: They also fine-tuned the Cluster Autoscaler to be more aggressive about scaling down. They reduced the
scale-down-unneeded-timeparameter from the default 10 minutes to 5 minutes, ensuring that unused nodes were terminated more quickly after a traffic spike subsided.
3. Strategic Integration of Spot Instances
This was the single largest driver of cost savings. Spot instances offer discounts of up to 90% over on-demand prices but can be interrupted at any time.
- Action: The team identified which workloads were “stateless” and fault-tolerant (e.g., the web front-end, image resizing services, data analytics jobs). These applications could handle an unexpected shutdown and restart without issue.
- Implementation: Using Karpenter, an open-source cluster autoscaler, they configured their EKS cluster to maintain a mix of node types. Critical stateful workloads (like databases) were set to run only on On-Demand nodes, while the stateless applications were configured to run on a fleet of Spot Instances. Karpenter automatically managed the provisioning and replacement of interrupted spot nodes, ensuring application resilience.
The Results: Quantifying the 30% Reduction
The combination of these strategies yielded dramatic and measurable savings.
| Optimization Strategy | Before Monthly Cost | After Monthly Cost | Savings | % of Total Saving |
| Baseline Cost | $85,000 | – | – | – |
| Right-Sizing | – | $76,500 | $8,500 | 10% |
| Autoscaling Tuning | – | $72,000 | $4,500 | 5% |
| Spot Instance Integration | – | $60,000 | $12,000 | 15% |
| Total | $85,000 | $60,000 | $25,000 | ~30% |
The project successfully reduced their monthly spend by approximately $25,000, confirming that a systematic approach to efficiency can have a massive financial impact.
Actionable Insights for Your Organization
AfriMart’s success provides a clear blueprint for any organization looking to rein in Kubernetes costs.
- Trust Data, Not Guesses: Don’t rely on developer estimates for resource requests. Implement robust monitoring and use actual usage data to drive your right-sizing efforts. This is the easiest and fastest way to achieve initial savings.
- Scale on What Matters: Don’t assume CPU is your only bottleneck. Analyze your applications and configure your pod autoscalers to respond to the metrics that actually signal load, such as queue depth, API latency, or active user connections.
- Embrace Interruptible Workloads: The biggest savings lie in changing how you pay for compute. Identify your stateless, fault-tolerant applications and make a plan to migrate them to Spot Instances. The risk is manageable with modern tools like Karpenter, and the financial reward is significant.
