Are you paying for “ghost” compute capacity that your applications never actually use? Most AWS environments operate at a meager 30–40% utilization, meaning nearly two-thirds of your cloud budget could be subsidizing idle silicon. Automated rightsizing eliminates this waste by matching resources to actual demand on autopilot.
Automated AWS rightsizing leverages machine learning and programmatic execution to perfectly align your cloud resource right-sizing with real-time workload demands. While manual rightsizing often feels like a losing game of “whack-a-mole,” automation allow you to eliminate over-provisioning across EC2, EBS, and Kubernetes without requiring dedicated engineering sprints or manual oversight.
Why manual rightsizing fails at scale
In a dynamic AWS environment, workload requirements change by the hour, meaning a manual audit performed today is often obsolete by next Tuesday. Engineering teams typically struggle with manual rightsizing because of a natural aversion to risk. To avoid performance bottlenecks or Out of Memory (OOM) errors, engineers often maintain “safety buffers” that represent 30–50% of compute waste.
Beyond risk, bandwidth constraints make manual optimization a low-leverage task that drains high-value engineering time. Identifying, testing, and resizing hundreds of instances is simply not a sustainable practice for modern teams. Furthermore, determining the “right” size requires analyzing 2–4 weeks of historical CPU, memory, and IOPS data – a data-heavy process that is difficult to replicate across thousands of resources. Hykell’s automated cloud cost management solves these issues by moving from periodic audits to 24/7 autonomous optimization.
Leveraging AWS Compute Optimizer as your data engine
The foundation of any automated strategy is AWS Compute Optimizer. This native tool uses machine learning to analyze historical utilization metrics from Amazon CloudWatch, categorizing resources as optimized, over-provisioned, or under-provisioned. Over-provisioned resources are those where the instance is larger than necessary, while under-provisioned resources represent a performance risk that requires more capacity.
Because approximately 82% of cloud-hosted Kubernetes workloads run on AWS, the ability of Compute Optimizer to analyze EC2, Auto Scaling groups, and ECS services on Fargate is essential. However, the tool only provides recommendations; it does not execute the changes. This is where Hykell’s automation steps in to bridge the “action gap,” taking the insights provided by AWS and implementing them across your infrastructure.
Automated EC2 and EBS optimization
To achieve a total AWS cost reduction of up to 40%, automation must target the two largest spend categories: compute and storage.
EC2 instance right-sizing
Automation platforms analyze P99 utilization data to identify candidates for downsizing with high precision. For example, moving a workload from a t3.xlarge to an r6g.large can yield savings of 40% for memory-intensive applications. Furthermore, migrating to AWS Graviton instances can offer up to 40% better price-performance over comparable x86 instances, allowing you to stack savings on top of your existing Reserved Instances and Savings Plans.
EBS volume tuning
Storage often accounts for 25–30% of the total cloud bill. Automation can identify “zombie” resources – unattached EBS volumes that still incur costs – and automatically migrate gp2 volumes to gp3. According to recent industry benchmarks, transitioning to gp3 is approximately 37–40% cheaper than gp2, providing an immediate ROI with zero performance degradation for your production workloads.
Automating Kubernetes (EKS) for maximum density
Kubernetes is notorious for hidden waste due to poorly defined pod requests and limits. Automated Kubernetes cost optimization addresses this through three primary pillars:
- Pod Right-sizing: Automatically adjusting CPU and memory requests to match actual usage can reduce cluster costs by 30–50% by improving pod density.
- Intelligent Node Scaling: Utilizing tools like Karpenter allows your environment to “bin-pack” pods onto the most cost-effective instance types in real-time, reducing overall node count.
- Spot Instance Integration: For non-critical or fault-tolerant workloads, automated systems can shift capacity to Spot Instances for up to 90% savings, while maintaining a fallback to On-Demand instances to preserve uptime if an interruption occurs.
Implementation: from insights to autopilot
Transitioning to automated rightsizing requires a phased approach to build trust and ensure application stability across your organization. You should begin with a thorough Hykell cost audit to identify your top 20% of spenders, which usually account for 80% of your total waste. Once you have a baseline, you must establish guardrails, such as defining “safe” maintenance windows and performance thresholds – targeting 60-70% average CPU utilization – to prevent over-aggressive downsizing during peak periods.
With guardrails in place, you can deploy continuous execution that identifies mismatches and implements changes during non-peak hours. Finally, it is crucial to close the feedback loop by monitoring the impact of right-sizing through real-time observability dashboards. This ensures you can validate that performance remains stable while your cloud costs drop month over month.
Put your AWS savings on autopilot
The most effective FinOps strategy is one that doesn’t require constant manual intervention from your engineering team. By combining the data-driven insights of native AWS tools with Hykell’s automated execution, you can stop over-paying for unused capacity and reclaim up to 40% of your AWS budget.
Our model is designed to be risk-free: we only take a slice of what you save. If we don’t uncover hidden efficiencies and reduce your bill, you don’t pay. Get a free cost assessment today and see how much you could save by putting your AWS rightsizing on autopilot.