While high-frequency alerting is sometimes justified for production SLAs, it's often overused across non-critical alerts or replicated blindly across environments. Projects with multiple environments (e.g., dev, QA, staging, prod) often duplicate alert rules without adjusting for business impact, which can lead to alert sprawl and inflated monitoring costs.

In large-scale environments, reducing the frequency of non-critical alerts—especially in lower environments—can yield significant savings. Teams often overlook this lever because alert configuration is considered part of operational hygiene rather than cost control. Tuning alert frequencies based on SLA requirements and actual urgency is a low-friction optimization opportunity that does not compromise observability when implemented thoughtfully.

Kubernetes environments often accumulate unused resources over time as applications evolve. Common examples include Persistent Volume Claims (PVCs) backed by Azure Disks, Services that trigger load balancer provisioning, or stale ConfigMaps and Secrets. When the associated deployments or pods are removed, these resources may remain unless explicitly deleted.

In AKS, this can lead to unmanaged costs, such as idle managed disks from orphaned PVCs or public load balancers from Services of type LoadBalancer. Even lightweight resources like unused Secrets or ConfigMaps degrade cluster hygiene and can introduce security or operational risk. This inefficiency is common across Kubernetes environments and is scoped here to AKS.

As organizations migrate from the Basic to the Standard tier of Azure Load Balancer (driven by Microsoft’s retirement of the Basic tier), they may unknowingly inherit cost structures they didn’t previously face. Specifically, each load balancing rule—both inbound and outbound—can contribute to ongoing charges. In applications that historically relied only on Basic load balancers, outbound rules may never have been configured, meaning their inclusion post-migration could be unnecessary.

This inefficiency tends to emerge in larger Azure estates where infrastructure-as-code or templated environments create load balancers in bulk, often replicating rules without review. Over time, dozens or hundreds of unused or outdated rules can accumulate, inflating network costs with no operational benefit.

S3 Storage Lens Advanced provides valuable insights into storage usage and trends, but it incurs a recurring cost. Organisations often enable it during an optimization initiative but fail to turn it off afterwards. When no active storage efficiency efforts are underway, these advanced metrics can become an unnecessary expense, especially at large scale across many buckets.

Advanced metrics include things like:

Cost optimization recommendations

Data retrieval patterns

Encryption and access control analytics

Historical trends beyond the 30-day free tier

Because the configuration is global or account-level, it’s easy to forget that these additional metrics are enabled and quietly incurring cost. This inefficiency often surfaces in organizations that over-invest in observability tooling without aligning it to an ongoing operational workflow.

In non-production environments—such as development, testing, and experimentation—many teams default to on-demand nodes out of habit or caution. However, Databricks offers built-in support for using spot instances safely. Its job scheduler and cluster management system are designed to detect spot instance evictions and automatically replace them with on-demand nodes when necessary, making the use of spot compute relatively low-risk.

Failing to enable spot for non-critical or short-lived workloads leads to unnecessary overspend. The inefficiency often arises because spot usage is not enabled by default and must be explicitly selected in cluster settings. In teams that don’t revisit infrastructure defaults or where FinOps guardrails are missing, this results in a persistent cost gap between actual usage and what could be safely optimized.

Clusters often accumulate unused components when applications are terminated or environments are cloned. These include PVCs backed by Managed Disks, Services that still front Azure Load Balancers, and test namespaces that are no longer maintained. Node pools are frequently overprovisioned, especially in multi-tenant or CI environments.

The cost impact of these idle resources is magnified in organizations with many environments or without standardized cleanup routines. Since billing is resource-specific, even low-cost items like Managed Disks, load balancer rules, and frontend configurations can accumulate meaningful waste over time.

As environments scale, GKE clusters tend to accumulate artifacts from ephemeral workloads, dev environments, or incomplete job execution. PVCs can continue to retain Persistent Disks, Services may continue to expose public IPs and provision load balancers, and node pools are often oversized for steady-state demand. This results in cloud spend that is not aligned with application activity.

Organizations that lack visibility into Kubernetes-level resource usage often miss these inefficiencies because GCP billing tools surface usage at the infrastructure level, not the Kubernetes object level.

Accelerated EC2 instance types such as `p5.48xlarge` and `p5en.48xlarge (often used for ML/AI workloads)` are eligible for Compute Savings Plans, but the discount rates offered are modest compared to more common instance families. When organizations rely solely on CSPs, these lower priority instances are typically the last to benefit from the plan, especially if other instance types consume most of the discounted hours.

As a result, p5 usage may fall through the cracks and be billed at full On-Demand rates despite an active CSP. This dynamic makes CSPs a potentially inefficient choice for workloads that heavily or predictably rely on these instance types. EC2 Instance Savings Plans provide better discount targeting for known usage patterns, and AWS now offers dedicated P5 and P5en Instance Savings Plans with up to 40% savings specifically for these instance types. Additionally, while Capacity Blocks offer the steepest discount, they come with operational rigidity and inflexible scheduling constraints that limit their applicability.

Many organizations choose a VM SKU and version (e.g., `D4s_v3`) during the initial planning phase of a project, often based on availability, compatibility, or early cost estimates. Over time, Microsoft releases newer hardware generations (e.g., `D4s_v4`, `D4s_v5`) that offer equivalent or better performance at the same or reduced cost. However, existing VMs are not automatically migrated, and these newer versions are often overlooked unless intentionally evaluated.

This inefficiency tends to persist because switching to a newer version typically requires VM deallocation and resizing, which introduces temporary downtime. As a result, outdated VM series versions continue to run indefinitely, even in environments where brief downtime is acceptable. The cost delta between series versions—especially across certain families like `D`, `E`, or `F`—can be significant when scaled across environments or multiple VMs. Note that VM series versions (v3, v4, v5) are distinct from VM generations (Gen 1 vs Gen 2), with series versions representing the primary opportunity for cost optimization.

In GKE environments, it is common for unused Kubernetes resources to accumulate over time. Examples include Persistent Volume Claims (PVCs) that retain provisioned Persistent Disks, or Services of type LoadBalancer that continue to front GCP external load balancers even after the backing pods are gone. ConfigMaps and Secrets may also linger, creating operational overhead.

These orphaned objects often persist due to gaps in CI/CD teardown logic, manual testing workflows, or drift over time. While some carry negligible cost on their own, others can result in significant charges, especially storage and networking artifacts. This inefficiency applies broadly across Kubernetes platforms and is scoped here to GKE.