Workload requests and limits
Compare configured CPU and memory with usage, throttling, restarts and peak demand. Identify changes that need workload testing before rollout.
Production Kubernetes consulting
Find what is holding your Kubernetes workloads back.
Investigate unstable autoscaling, pending pods and wasted capacity with Anrotex. Get a review that connects workload demand, cluster configuration and application health to a practical improvement plan.
Free initial consultation · Workloads and access agreed upfront
When a cluster review helps
For engineering teams running production workloads whose scaling behavior, reliability or infrastructure spend needs investigation.
- HPA adds replicas, but new pods stay pending or unready.
- Latency or errors increase during traffic bursts or deployments.
- Nodes remain expensive while workloads use little of the reserved capacity.
- Autoscaling changes are hard to explain or keep reversing.
What we review
The scope follows your symptoms and the controllers you operate. We agree the environments and representative workloads before collecting evidence.
Autoscaling and metrics
Review HPA targets, metric availability, replica bounds and stabilization. Check how VPA recommendations or updates interact with workload scaling where VPA is installed.
Node capacity and placement
Review node pools, Cluster Autoscaler or Karpenter settings where used, scheduling constraints, provisioning delays and idle requested capacity.
Availability during change
Review readiness and startup probes, termination, rollout settings and disruption budgets alongside application and dependency limits.
Observability and response
Connect scaling events to latency, errors, queue depth and pending pods. Identify missing signals and runbook steps needed to diagnose the next incident.
Cost and validation
Relate node and workload costs to demand. Separate potential savings from changes that could reduce reliability, and define checks for proposed adjustments.
What your team receives
Prioritized findings
Each finding identifies the affected workload, supporting evidence, operational risk and recommended next action.
A workload-specific plan
Recommended configuration changes, the order to test them, and any issues that need further investigation.
Testing and team handover
A validation checklist covering scaling response, readiness, service health and cost, with a walkthrough for the engineers operating the cluster.
What to share before a review
Share the cloud provider, Kubernetes version, approximate workload count and one recurring symptom. Useful discovery material includes redacted manifests, a recent incident timeline, scaling graphs and workload cost reports.
We agree read-only access where needed for the review. Keep credentials, tokens and kubeconfig files out of the contact form.
Implementation and ongoing support
The review provides findings and a plan. Production changes, load generation, node drains, upgrades and migrations require an agreed implementation scope and recovery procedure.
For implementation, validate representative workloads in a test environment, agree service health thresholds, and roll out changes in controlled steps. Continuous monitoring and incident response are scoped separately.
Work through an autoscaling example
Our Kubernetes guide explains a replica calculation, an HPA manifest, troubleshooting commands and a validation checklist.
Read the Kubernetes scaling guideConnect cluster changes with wider infrastructure
EKS spend may be one part of a broader cloud bill. Release workflows and Terraform may also need attention.
Questions before a cluster review
What is included in a Kubernetes cluster review?
We agree the clusters and workloads in scope, then review resource settings, autoscaling, node capacity, availability controls, observability and relevant costs. The output is a prioritized findings report and validation plan. Production implementation is scoped separately.
What information and access do you need?
Discovery can begin with redacted manifests, architecture diagrams, incident examples and dashboard exports. A deeper review may need agreed read-only cluster, metrics and cloud billing access. Do not send kubeconfig files, tokens or secrets through the enquiry form.
Can you fix HPA issues or reduce EKS costs?
We investigate the connection between resource requests, metrics, replica changes, scheduling and node capacity. Recommendations may address HPA, VPA, node provisioning or workload sizing. We estimate potential savings from your workload demand and reliability requirements.
Do you support production clusters across different clouds?
Yes. We work with production Kubernetes on AWS, Google Cloud and Azure. The review is scoped to the cluster version, managed services and controllers you actually run. Platform upgrades or migrations require their own agreed scope.
How do implementation, pricing and ongoing support work?
The initial consultation is free. Review pricing and timing depend on workload count, evidence availability and complexity. Implementation includes an agreed test and recovery plan. Ongoing monitoring, incident response and further changes are separate commitments.
Turn a scaling symptom into a testable plan.
Tell us what happens when demand changes, what your team has tried and what a successful outcome would look like.
Discuss a cluster review