Your biggest untapped lever for efficiency and reliability
JVM on Kubernetes Optimization Autonomous, Continuous, and Full-Stack
Akamas continuously analyzes your JVM workloads and optimizes heap, garbage collection, and Kubernetes Pods & HPA policies – so your Java applications run reliably, efficiently, and at the lowest sustainable cost.
-58%
CLOUD COSTS
-40%
MEMORY
-68%
CPU
<2 wks
TIME TO VALUE
Proven in production with Sisal. Read the case study
THE CHALLENGE
Three Pain Points every Java team knows.
Default behavior of JVMs was meant for large static machines, Kubernetes is not that environment.
OOMKilled Pods
JVM memory far exceeds heap – metaspace, code cache, and native memory push usage 30-100% beyond -Xmx, causing repeated crashes
Slow Startup
JIT, classpath scanning, and bean init demand heavy CPU. Spring Boot takes 30~60s+ with tight limits – defeating autoscaling.
Cloud Waste
62% say over half of cloud costs are Java. 71% report 20%+ capacity unused. Fear of OOMKilled makes right-sizing impossible.
HOW AKAMAS WORKS
Full-Stack Optimization, from signals to action
Too often, the disconnect between JVM configuration and Kubernetes resources is what crashes
everything. To prevent that, over-provisioning has become the norm, Akamas changes this by analyzing
and optimizing across the full stack – continuously.
DIAGNOSE
Surface what’s wrong and why it matters
Akamas automatically identifies efficiency, reliability, and best-practice issues
across your JVM and Kubernetes configuration. Each finding is categorized and
severity-ranked so teams know where to focus.
Findings like “memory utilization too close to limits” or “JVM running with default
configuration” connect runtime behavior to infrastructure risk – bridging the gap
between development and operations teams.
ANALYZE
See what’s really happening at every layer
Akamas shows how CPU, memory, and heap are actually used versus what’s
allocated – across all pods. The gap between configured limits and real utilization is where the waste lives.
Heap sizing influences pod density. GC behavior affects CPU usage and
autoscaling signals. Akamas makes these interactions visible, so you stop
guessing and start deciding with data.
Unlike tools that focus only on containers or only on the runtime, Akamas captures the real interaction between JVM configuration, Kubernetes resources, and workload behavior.
OPTIMIZE
JVM and Kubernetes, tuned together
Akamas delivers full-stack recommendations: JVM heap, GC selection, and HPA
policies alongside container CPU and memory – because tuning one layer
without the other is what causes the next outage.
Every recommendation shows the expected impact, and teams retain full control over what gets applied. The result is a repeatable optimization process, not a one-off tuning effort.
While JVM distributions improve runtime internals, configuration decisions across heap, CPU limits, and scaling policies still determine real-world efficiency. Akamas optimizes those decisions across the full stack.
Traditional tuning relies on static rules. Akamas explores alternatives autonomously, guided
by measurable objectives and validated against real workload behavior.
Akamas helps us size our pods correctly and address configuration issues that often emerge with new services. It also fills a cross-team skills gap between developers and our Kubernetes administrators, delivering significant reliability improvements, and the cost savings donʼt hurt either.
Gabriele Bosisio
Head of Operations Reliability & Security, Sisal
Akamas helped us to rapidly mature on the performance tuning front, by allowing us to find an optimal configuration for our application. This resulted in significant cost savings as well as removing barriers to replatforming.
Chris Cholette
VP Productivity and Site Reliability Engineering, Navan
Thanks to Akamas, TeamSystem has improved the efficiency of our critical microservices as we would never be able to do manually. The ability to consistently deliver the highest level of quality to our end-users at the lowest possible cost is an important differentiator for us.
Luca Montecchiani
Lead Software Architect, Product Owner, TeamSystem
Within just a few hours, Akamas uncovered performance issues we had overlooked for months. This wasn’t just an improvement – it was a revelation. Akamas delivered insights we didn’t even know we needed and solved problems faster than any manual approach could.
Damjan Kumin
Chief Technology Officer, Perform IT
Akamas’ ease of integration with our CI/CD pipelines enabled us to automate the configuration deployment to quickly find optimized configurations that had not been previously found with our manual approach.
Gartner Peer Insights
Customer in Online Services
BLOG
JVM Optimization Resources
See for Yourself
Experience the benefits of Akamas autonomous optimization.
No overselling, no strings attached, no commitments.