An Anexia Kubernetes cluster scales to forty pods during a retry bug — bill doubled, database still saturated. Nobody had set maxReplicas, chosen the region closest to users, or tested scale-down. Autoscaling obeyed; governance did not.
Anexia Kubernetes suits European workloads sensitive to location. Region and limits are decided before the first Horizontal Pod Autoscaler — not after the first surprise invoice.
Choosing the region: three dimensions
Region is not ping alone. Cross three axes before provisioning.
Users: measure round-trip latency from your target market — Paris, Berlin, Vienna as applicable. Data: GDPR and client contracts may require specific residency; document the choice in your register if personal data flows through. Catalog: GPU, block storage, instance types — not everything is available in every Anexia site.
| Criterion | Decision question |
|---|---|
| Users | Round-trip latency from target market |
| Data | GDPR, contractual residency |
| Services | GPU, block storage available in region? |
| Recovery | Backup on another Anexia site or external |
| Support | NOC timezone and language |
Document the chosen region in the compliance register if personal data is processed.
Limits before autoscaling
Horizontal autoscaling amplifies your configuration — good or bad. Set five guardrails before production.
Define requests and limits per deployment: HPA reads real metrics, not intentions. Set explicit maxReplicas (ten, not a hundred). Configure a Pod Disruption Budget to avoid total drain during updates. Cap cluster autoscaler max nodes if enabled. Alert on pending pods, CPU throttling, and OOMKilled events.
Test scale-up and scale-down: reduction is sometimes blocked by a miscalibrated PDB or undersized persistent volumes.
Regional observability
Without metrics, HPA becomes a black box. Deploy Prometheus per cluster with region labels. Centralise logs off-cluster and test restore. Correlate inter-zone latency if you use multiple availability zones.
Observability is not a luxury on Kubernetes: it is the only way to know whether autoscaling fixes a legitimate spike or amplifies an application bug.
The climax: autoscale without caps scales errors
Choose region and caps before the first kubectl apply on HPA.
Decide and move forward without blind spots
First validate the region on latency and compliance criteria, then write down HPA and cluster autoscaler quotas. Run a load test with scale-up and scale-down before production. Document etcd and persistent volume backup strategy. Finally, review the Anexia profile and compare tool to situate the offer against your real needs.
Frequently asked questions
Which Anexia regions for Kubernetes?
Austria and European extensions depending on offer — verify instance types and latency at provisioning time, not after the fact.
Should you cap HPA before production?
Yes: maxReplicas, requests/limits, and Pod Disruption Budget avoid infinite scale during incidents. Without caps, you pay for application errors at compute prices.
Multi-region from day one?
Often no — single region with tested backups is enough initially. Multi-region is justified when RTO requires it and the team can handle network and state complexity.
Difference vs hyperscaler?
More European sovereignty and nearby support; fewer integrated managed services. You carry more operations but control scope and location better.
Set region and maxReplicas in the same architecture document — autoscaling will not fix the absence of both.