Current and proposed
A current observation and a historical recommendation tell different parts of the story. Both should be clear.
Evidence-driven AKS optimization
Understand what your workloads actually need.
Evidence-backed CPU and Memory recommendations for Azure AKS. Understand where a change may help, where the current request fits, and when more evidence is needed.
For Azure AKS workloads. Agentless assessment. Your team controls changes.
RATIVERA / Workload evidenceIllustrative example
Deployment · demo workload
checkout-api
CPU
1000mCurrent request
↓ DOWNSIZE600mProposed request · per replica
MEMORY
512MiBCurrent request
↑ UPSIZE768MiBProposed request · per replica
Reliability comes first.
This illustrative proposal shows different directions for CPU and memory. Actual recommendations require sufficient evidence and applicable constraints.
Human decision PENDINGInfrastructure changes NONE
One Problem at a Time · Episode 1 · 1:15
Are your Kubernetes workloads requesting more resources than they need?
Why requests and usage differ, and why a low average alone cannot justify a reduction.
Illustrative numbers, not a customer result or savings claim. Requests affect scheduler placement; existing containers can still use otherwise idle CPU. In the CPU example, observed peaks are based on sampled five-minute averages. Seven days is the reduction-history gate described here; protective memory increases can follow a different path.
English captions are burned into the video. An optional English caption track and transcript are also available. Use fullscreen for small screen details.
01 Evidence determines sizing02 Reliability before reduction03 You remain in control
From assumptions to understanding
A request is a decision.
Give it a reason.
Inherited defaults. Last year’s peak. A generous buffer that nobody wants to touch.
Kubernetes requests often outlive the assumptions behind them. RATIVERA helps you examine the workload behavior and context behind a sizing decision.
When the evidence does not support a recommendation, the useful answer is an explanation of what is missing.
Explore the productThe stewardship loop
Observe carefully.
Recommend with reasons.
01
Observe
Measure workload behavior.
02
Learn
Build historical context.
03
Recommend
Explain a supported proposal.
04
Review
Preserve the human decision.
05
Act
Your change process.
06
Explain
Keep evidence and reasoning visible.
Assessment and human decisions are separate from applying changes. No automatic infrastructure changes.
Understand each stepEvidence, in context
See the conclusion.
Keep the reasoning close.
Protection and uncertainty
Understand observed demand, protected historical demand and the gaps that can withhold a recommendation.
Decisions with history
Accept, reject or defer. Preserve the decision and rejection rationale without silently changing the sizing model.
RATIVERA observes and recommends.
It never directly changes your running workloads.
Explore Security & TrustA bounded evaluation
Start with your environment.
Review your AKS scope and monitoring prerequisites. Explore the 14-day Trial. Registration will open only after Production readiness is confirmed and the founder authorizes onboarding.
Built for teams running AKS.
For platform engineers, DevOps engineers, and engineering managers who need to explain a resource decision before making a change.
No RATIVERA agent in your AKS cluster. The AKS path reads authorized Azure resource facts and supported monitoring data; Azure Managed Prometheus and retained metrics are prerequisites.
Choose the workload. Keep the decision.
Discovery shows what is present. Explicit workload selection records what your team wants assessed. A recommendation still depends on usable evidence and the applicable checks; selection is not a promise that a recommendation will be available.
Make room for better decisions
Start with the evidence.
Understand the reasoning before you change the request.
See how RATIVERA works
Evaluation availability and terms must be confirmed before you connect.