Platform engineers rely on the Kubernetes cluster autoscaler to control cloud cost. However, the autoscaler is only as efficient as the node instances it provisions. When the instance type doesn’t match the workload shape, resources get stranded. The cluster autoscaler then replicates that waste at every scale-up event.
Akamas Insights solves this with a Node Instance Optimizer. It analyzes actual workload behavior across each node pool and recommends the best instance family and size. Akamas benchmarks show 15–20% monthly cloud cost reduction from node instance optimization alone without changing application code (Source: Akamas Insights internal data, Q1 2026).
The root of the problem is architectural. Most teams default to general-purpose instances. They treat Kubernetes autoscaling as a cost-control mechanism. But real cluster density requires matching your infrastructure shape to your workload shape. The cluster autoscaler was never designed to do that on its own.
Why the Kubernetes Cluster Autoscaler Fails: The Bin Packing Problem
To combat rising cloud costs, teams turn to the Kubernetes cluster autoscaler. The logic is simple: add nodes when workloads need them, remove nodes when they don’t. The problem? The autoscaler only provisions more of whatever instance type the node pool already uses.
Consider a common scenario. Your pods are compute-intensive but light on memory. They run on general-purpose instances. The node runs out of CPU while gigabytes of memory sit unused. Kubernetes can’t schedule new pods on a node without enough CPU. That unused memory becomes stranded capacity; no workload can access it.
How stranded resources multiply
This creates a bin packing problem. The cluster autoscaler provisions more nodes of the same shape. Each new node carries the same wasted capacity. Cluster density drops and cloud spend climbs.
The autoscaler didn’t fail at its job. Instead, the foundation it scales from was never optimized. The autoscaler handles scaling. Instance type selection determines whether that scaling is efficient or wasteful.
In practice, Kubernetes resource efficiency requires alignment between workload profiles and node instance types. Without that alignment, automatic node provisioning just multiplies the mismatch.
How Akamas Optimizes Node Instances for Each Node Pool
Akamas Insights addresses this gap with the Node Instance Optimizer. The capability is built for platform engineers who manage multi-node-pool clusters.
Rather than relying on utilization averages, the optimizer analyzes actual workload behavior. It then determines the best instance family and size for each individual node pool.
Specifically, Akamas Insights examines three critical inputs that manual analysis typically misses:
- Pod resource profiles: the CPU and memory requests and limits of every deployed pod in the node pool.
- Scheduling constraints: the requirements of unusually large pods that dictate minimum node sizes.
- Node overheads: allocatable limits and cloud provider constraints on CPU, memory, and MaxPods.
Based on this analysis, the optimizer recommends shifting each node pool from a general-purpose default to a purpose-matched instance type.
For instance, migrating a node pool from m6g.2xlarge to c6g.4xlarge might seem complex. But data-driven analysis shows these workloads are compute-bound with low memory use. This shift eliminates stranded memory and improves pod scheduling efficiency. The result is the right infrastructure shape for the actual workload profile.
Want to see what node instance optimization finds in your cluster? Start a free trial — connect your observability stack in minutes, no agents, no code changes.
Full-Stack Savings: 15–20% Cloud Cost Reduction Without Code Changes
By matching the instance type to the actual workload profile, organizations maintain CPU efficiency while recovering significant memory. Akamas benchmarks show a single optimized node pool typically recovers 200–400 GiB of stranded memory (Source: Akamas Insights internal data, Q1 2026). Better node utilization means fewer nodes and lower cloud spend.
Why full-stack matters
A Kubernetes workload runs on three layers: the application runtime (JVM, Node.js), the pod resource configuration (CPU and memory requests), and the node instance type. These layers depend on each other.
A well-chosen instance type still wastes resources if the pod requests too much memory. A right-sized pod still gets OOMKilled if the JVM heap conflicts with the Kubernetes memory limit.
This is why optimizing one layer at a time doesn’t work. Akamas Insights optimizes all three together: it tunes the runtime, right-sizes the pod resources, and selects the best node instance type, using actual telemetry from your cluster. When all three layers align, the cluster autoscaler finally scales density instead of waste.
For more on common pitfalls, see our guide to Kubernetes optimization best practices.
Explore the Akamas documentation for a full walkthrough of how pod, runtime, and node optimization work together.
Cluster Autoscaler vs. Karpenter: Two Approaches to Node Scaling
The Kubernetes cluster autoscaler and AWS Karpenter both handle node-level scaling. However, they solve different problems.
The cluster autoscaler works with predefined node groups. It adds or removes nodes from existing pools based on pending pods. Karpenter takes a different approach. It provisions individual nodes with the right instance type for each pending pod. It bypasses node groups entirely.
Where Akamas fits
Karpenter is strong at choosing the right node when a pod needs to be scheduled. However, it only sees what the pod requests at that moment. It doesn’t know whether those resource requests are well-tuned, or whether the application runtime inside the container is configured efficiently.
Akamas Insights works from the opposite direction. It observes how workloads actually behave over days and weeks: how much CPU and memory they consume in practice, how runtime settings affect resource use, and where the gap sits between what pods request and what they need. Based on that observed data, it recommends the right instance type for each node pool.
For organizations using the cluster autoscaler, Akamas addresses the root cause of waste: wrong instance types that strand resources at every scale-up. For organizations using Karpenter, Akamas complements the provisioner. It optimizes the pod resource settings and runtime configurations that Karpenter uses as inputs for its decisions.
The key difference: Karpenter optimizes provisioning decisions at scheduling time. Akamas optimizes the workload configurations and infrastructure foundations that determine whether those decisions are efficient.rmine whether those provisioning decisions are efficient.
See how Akamas complements your current scaling setup. Book a demo with our team, or start a free trial to test it against your own cluster data.
FAQs
The Kubernetes cluster autoscaler adds or removes nodes based on pending pod scheduling needs. It watches for pods that can’t be scheduled due to resource limits. Then it provisions new nodes to make room. However, the autoscaler does not evaluate whether the node instance type fits the workloads running on the cluster.
The autoscaler provisions the same instance type configured for the node pool. It ignores actual workload profiles. When pods need lots of CPU but little memory, general-purpose instances leave memory stranded. The autoscaler then adds more nodes of the same shape. Each new node replicates the same waste.
Node instance optimization matches the instance type to the workload’s actual resource profile. It analyzes CPU-to-memory ratios, scheduling constraints, and pod sizes. Then it recommends the right instance family. Akamas benchmarks show 15–20% monthly cost reduction by eliminating stranded resources (Source: Akamas Insights internal data, Q1 2026).
The cluster autoscaler scales predefined node groups up and down. Karpenter provisions individual nodes with instance types chosen per pod at scheduling time. Both handle node scaling. Neither optimizes the workload configurations — pod resources, runtime settings — that determine whether scaling is efficient. Akamas Insights optimizes those workload-level settings using observed data.
Yes. Node instance optimization works at the infrastructure layer. It changes the instance type for each node pool. Your applications keep running unchanged, but on infrastructure that better matches their resource needs.
Pod rightsizing adjusts CPU and memory requests at the container level. Node instance optimization selects the right infrastructure shape at the node pool level. Akamas Insights optimizes both together, along with runtime settings. This delivers compound savings across the full Kubernetes stack.
