Insights

Introduction

In cloud-native platforms, peak periods put both technology and operational readiness to the test. Increased traffic does not just strain capacity; it often exposes hidden assumptions within Kubernetes architectures that can impact business-critical outcomes.

During a recent review of a client’s Azure Kubernetes Service (AKS) platform ahead of a peak period, our objective was to provide confidence that the platform could handle the expected load. Achieving this assurance required a structured approach that went beyond individual services to examine how workloads, infrastructure, monitoring, and operational processes behave under pressure.

This is how we approached it.

Understanding workload performance

Ensuring APIs deployed on AKS perform reliably under peak traffic is fundamental to maintaining responsiveness and availability. Without adequate validation, increased demand can introduce bottlenecks that lead to degraded performance or service disruption.

We reviewed API performance by closely monitoring key indicators such as throughput, error rates, and response times to confirm services remained responsive. At the pod level, CPU and memory utilisation were assessed to ensure sufficient headroom during scaling events.

A critical part of this review was validating Horizontal Pod Autoscaler (HPA) behaviour, confirming that pods would scale up and down appropriately as thresholds were reached. Where applicable, KEDA metrics were used to support HPA, enabling scaling decisions based on real-time, application-specific signals rather than CPU or memory alone. Pod-level metrics were also reviewed to identify any underperforming or overloaded pods that could affect overall service performance.

Together, these checks helped identify potential workload-level constraints before peak traffic arrived.

Validating cluster readiness

Even well-behaved workloads can fail if the underlying platform lacks capacity or resilience. With this in mind, we assessed the readiness of the AKS cluster itself.

Key infrastructure metrics were reviewed, including CPU and memory utilisation per node, to ensure sufficient capacity to support increased pod density. Disk I/O usage was monitored to identify potential storage bottlenecks that could affect performance under load. We also verified that the Cluster Autoscaler was available and correctly configured to add nodes when required. Finally, node capacity limits were reviewed to confirm adequate headroom for scaling during peak demand.

This ensured that the platform could support workload scaling without introducing infrastructure-level constraints.

Building effective monitoring

Although extensive monitoring was already in place, it was not optimised for peak operations. To address this, we implemented dashboards specifically designed for high-load periods using Prometheus and Grafana.

These dashboards consolidated the most critical signals needed during peak into a single view, including:

• API response times
• Error rates
• Replica counts
• Pod restart counts
• CPU and memory utilisation at node level
• Node capacity and available headroom

The dashboards were designed to be usable not only by engineers but also by operations and on-call teams, providing a shared, reliable source of truth during critical periods.

Incident response coordination

In parallel, we worked with the incident response team to strengthen their operational capability on the AKS platform. This included guidance on tooling, diagnostics, and platform-specific best practices, as well as facilitating closer collaboration with the AKS platform team.

As a result, additional dashboards and pipelines were introduced, improving the team’s ability to monitor, triage, and manage AKS-related incidents more effectively. This increased confidence and control during peak operational periods.

Conclusion

By systematically reviewing workload performance, validating cluster readiness, implementing targeted monitoring, and strengthening incident response capabilities, we provided the client with a clear, data-driven view of their platform’s readiness for peak demand.

This structured approach ensured that both APIs and infrastructure were prepared to handle increased load, while equipping operations teams with the visibility and confidence needed to respond quickly and effectively should issues arise.