Continuous optimization
Full-stack Kubernetes optimization: pods, runtimes, and nodes together
The idea
Akamas Vivo knows what good looks like
Akamas has spent years running tuning experiments across JVM, Node.js and Kubernetes stacks. Vivo compares what your workloads are doing against what those experiments established, then proposes a configuration and shows the evidence behind it.
Full-Stack Optimization
Savings From Day One
Proactive Reliability
Compliance By Design
Get started in minutes
How do you
connect Akamas Vivo?
Connect Akamas Vivo to the observability stack you already run, using tokens you issue and control. No agents, no code changes.
Within minutes, Vivo starts analyzing metrics from Prometheus, Dynatrace or Datadog to uncover inefficiencies and reliability risks across your Kubernetes environments.
Intelligent Prioritization
Which fixes matter most?
Akamas Vivo analyzes telemetry to find where resources are wasted or risks arise, from workloads to nodes.
Each finding includes an estimated impact, so teams can prioritize what matters most to performance, reliability and cost.
Cross-stack alignment
Why isn’t
pod rightsizing enough?
Modern applications don’t run directly on Kubernetes. They run on language runtimes like the JVM, the Node.js V8 engine or .NET, each with hundreds of configuration options that affect resource usage, reliability and cost.
Teams that focus only on pods are optimizing the container, not the application. Akamas goes further, analyzing configuration across every layer, from Kubernetes pods and HPA policies to JVM heap settings and V8 garbage collection, to surface the interactions that infrastructure metrics alone will never reveal.
Explore application runtimes Explore application runtimes
JVM on Kubernetes optimization
How do you stop over-provisioning Java on Kubernetes?
The JVM runs on default settings that were never designed for containers. Heap misconfiguration, aggressive garbage collection and slow JIT warm-up cause OOM kills and CPU throttling that right-sizing pods alone can never fix.
Akamas Vivo continuously analyzes JVM heap, garbage collection and Kubernetes pod resources together, and recommends the settings that eliminate waste without compromising reliability.
At Sisal, Akamas cut microservice cloud cost by 58%, memory by 40% and CPU usage by 68%, in production, without a single line of code change.
Node.js Runtime Optimization
How do you tune Node.js on Kubernetes?
V8’s default heuristics are rarely tuned for containers, which leaves many Node.js services stuck in excessive garbage collection cycles or at risk of OOM kills.
Akamas Vivo analyzes V8 heap settings alongside Kubernetes pod limits to find the right runtime configuration, with no code changes.
In Akamas benchmarks, tuning V8 old-space settings delivered up to a 45% performance increase and a 68% reduction in CPU usage.
See the benchmark See the benchmark
HPA and KEDA
How do you optimize pods that autoscale?
Autoscaling from an over-provisioned pod replicates waste at scale.
Akamas Vivo supports HPA and KEDA workloads. It optimizes the resource baseline your autoscaler scales from, without touching your scaling thresholds.
In Akamas Vivo optimization data from customer environments, fixing the HPA baseline cut compute waste by up to 35%.
How HPA baselines work How HPA baselines work
Policy-Driven Governance
Can you set different policies for different workloads?
Not every workload deserves the same optimization strategy. Tuning profiles let you apply aggressive cost-saving targets to dev namespaces and conservative safety buffers to tier-one services at the same time, from the same interface.
In Akamas internal benchmarks, cost-focused profiles cut cost by up to 45% in non-critical environments, and stability-focused profiles reduced OOM errors by 60% for production services.
How tuning profiles work How tuning profiles work
GitOps integration
How do recommendations reach your cluster?
Autonomous agents that bypass your delivery pipeline create shadow configurations developers can’t trace or roll back.
Akamas Vivo integrates with your GitOps source of truth. It opens pull requests with evidence-based configuration changes, including projected cost savings and latency improvements, for developer review. Every change is versioned, auditable and compatible with Argo CD or Flux.
Cluster node pool optimization
How do you right-size node groups?
Akamas Vivo analyzes how workloads use cluster resources. It evaluates instance families, sizes and real utilization to uncover over-provisioning and misaligned capacity ratios.
It recommends instance types for your node pools, so you get the most from your Cluster Autoscaler and Karpenter setups and reduce cost safely while keeping reliability.
HOW IT COMPARES
How is Akamas Vivo different from pod rightsizing tools?
Pod rightsizing platforms changed the first row. The rows underneath are where the difference is.
| Static requests and limits | Pod rightsizing tools | Akamas Vivo | |
|---|---|---|---|
| What it changes | Pod requests and limits, set by hand | Pod requests and limits | Pod resources, runtime settings, autoscaler baselines and node groups, as one set |
| Where the numbers come from | An estimate at deploy time | Observed usage | Observed usage, compared against what years of Akamas tuning experiments established |
| JVM, Node.js and .NET settings | Not covered | Varies by tool | Tuned together with the pod |
| Autoscaled workloads | Thresholds and baselines set by hand | Varies by tool | Baseline right-sized, HPA and KEDA thresholds left as you set them |
| Node group instance types | Not covered | Varies by tool | From the same analysis |
| How changes ship | Manual edits | Usually applied by the tool | Through your GitOps workflow: review and apply, or automate with guardrails |
| What you can show afterwards | Nothing | A usage chart | The finding, its estimated impact and the evidence behind the change |
PROOF
What results do teams get
with Akamas Vivo?
One published production result, and one analysis of a real outage replayed through Vivo.
- Memory
- −40%
- CPU usage
- −68%
- Code changes
- None
Outage analysis · Global retail platform
Preventing a Kubernetes outage before it happens
A global retail platform lost a business-critical application when node memory saturated after a routine release. The team raised node memory by 50% to recover, which treated the symptom.
Replaying the telemetry from before the crash, Akamas Vivo flagged the memory risks in advance and recommended a workload and JVM reconfiguration that would have prevented it, with no infrastructure change.
Read the analysis Read the analysisWho it’s for
Who is Akamas Vivo for?
Akamas Vivo is designed for professionals who prioritize performance and efficiency.
Platform Engineers
- Gain full visibility into cluster efficiency and autoscaling behavior.
- Detect and fix misconfigured workloads that affect cluster reliability.
- Improve platform adoption by helping developers deliver more efficient, reliable workloads, with no extra effort.
Developers
- Get application-aware recommendations, with no need to tweak JVM or Kubernetes settings.
- Avoid OOMs, CPU throttling and other performance incidents with zero manual tuning.
- Contribute to cost-saving goals without compromising application reliability.
SREs & DevOps
- Prevent incidents by spotting misconfigurations before they cause problems.
- Eliminate manual tuning with automation across workloads and runtimes.
- Address root causes, not just symptoms, by optimizing both Kubernetes and application layers.
Performance Engineers
- Detect runtime inefficiencies like suboptimal JVM settings.
- Use production insights to refine test scenarios.
- Help application teams adopt best practices for both performance and reliability.
AKAMAS STUDIO
Where does Vivo’s knowledge come from?
From years of tuning experiments, many of them run with Akamas Studio. Studio tests candidate configurations against your own performance tests in pre-production, for any stack with parameters, and keeps only the configurations that measured results prove.
