In the April 2026 edition: engineering cost-efficient microservices with backend FinOps, why Java pods keep hitting Kubernetes OOMKills, and the limits of standard AI cloud optimization. The Optimal is the Akamas newsletter where DevRel Engineer Graziano Casto breaks down Kubernetes performance, reliability, and cost optimization.
Hello everyone! Graziano here.
If you’re reading this, we are officially wrapping up April 2026 and heading straight into the summer conference season. We’ve been busy optimizing clusters in the lab and preparing for the next wave of events. Grab a coffee and let’s look at the logs for this month.
The Latest Replica
Backend FinOps: Engineering Cost-Efficient Microservices
In the microservices world, resource fragmentation is quietly draining our budgets. Because teams routinely size their pods for peak traffic instead of normal demand, average utilization often hovers below 20%. A recent InfoQ article highlights how these unoptimized allocations, along with serverless cold-start overheads and orphaned resources, compound into massive cloud bills. The fix isn’t just asking finance for more budget; it requires strict engineering discipline, automated scaling policies, and proper benchmarking to ensure you are only paying for what you actually use.
Java Pods and the K8s OOMKill Menace
If you run Java on Kubernetes, you’ve likely stared down the barrel of an OOMKilled exit code. As highlighted in a recent DZone piece, the clash between Java’s memory management and Kubernetes cgroups is a tale as old as time. While modern JVMs are container-aware, they are still routinely terminated by K8s OOMKills. This clash usually happens because container limits are sized only for the heap, ignoring the significant, often unpredictable footprint of off-heap memory. The classic, reactive FinOps “solution” is to just double the memory limit, wasting massive amounts of money across a cluster. The real engineering fix requires deep tuning of JVM flags (like MaxRAMPercentage) to respect container boundaries, proving once again that you can’t just throw infrastructure at an application problem.
The Limits of Standard AI Cloud Optimization
A recent article floating around the community explores using AI for multi-cloud resource optimization. It heavily focuses on predictive auto-scaling and rightsizing VMs based on historical metrics. While catching idle compute is a good start, it stops at the infrastructure layer. Standard AI tools will tell you to resize your node, but they won’t tell you how to configure the app running on it. That’s the real gap in the market today: resizing the box doesn’t fix a misconfigured JVM or a bottlenecked database. To achieve real efficiency, you have to tune the application parameters and the infrastructure together.
Commits From The Lab
Tackling Resource Fragmentation Head-On
The articles this month validate exactly what we’ve been building. If sizing for peak traffic is the problem, continuous and automated tuning is the answer. We recently pushed some exciting updates to our machine learning engines that help identify and eliminate the exact resource sprawl and orphaned capacity discussed above. By treating cost as a technical metric rather than a billing problem, we are helping platform engineering teams reclaim their budgets.
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Catch Us
Want to meet the Akamas team in person? We’re regularly at conferences and meetups across the Kubernetes and Java ecosystems. See where we’ll be next on our Events page, and follow me on LinkedIn for the talks and sessions I’ll be at.
Keep those CPUs cool and your latencies low.
Stay optimized,
Graziano Casto, DevRel @ Akamas
