In EKS environments, cluster sprawl can occur when workloads are removed but underlying resources remain. Common issues include persistent volumes no longer mounted by pods, services still backed by ELBs despite being unused, and overprovisioned nodes for workloads that no longer exist. Node overprovisioning can result from high CPU/memory requests or limits, DaemonSets running on every node, restrictive Pod Disruption Budgets, anti-affinity rules, uneven AZ distribution, or slow scale-down timers. Preventative measures include improving bin packing efficiency, enabling Karpenter consolidation, and right-sizing node instance types and counts. Dev/test namespaces and short-lived environments often accumulate without clear teardown processes, leading to ongoing idle costs.

These remnants contribute to excess infrastructure cost and control plane noise. Since AWS bills independently for each resource (e.g., EBS, ELB, EC2), inefficiencies can add up quickly. Without structured governance or cleanup tooling, clusters gradually fill with orphaned objects and unused capacity.

Azure provides VM families across three major CPU architectures, but default provisioning often leans toward Intel-based SKUs due to inertia or pre-configured templates. AMD and ARM alternatives offer substantial cost savings; ARM in particular can be 30–50% cheaper for general-purpose workloads. These price differences accumulate quickly at scale.

ARM-based VMs in Azure (e.g., Dps_v5, Eps_v5) are suited for many common workloads, such as microservices, web applications, and containerized environments. However, not all applications are architecture-compatible, especially those with dependencies on x86-specific libraries or instruction sets. Organizations that skip architecture evaluation during provisioning miss out on cost-efficient options.

By default, all Log Analytics tables are created under the Analytics plan, which is optimized for high-performance querying and interactive analysis. However, not all telemetry requires real-time access or frequent querying. Some tables may serve audit, archival, or compliance use cases where querying is rare or unnecessary. Leaving such tables on the Analytics plan results in unnecessary spend—especially when ingestion volumes are high or the table receives data from verbose sources (e.g., diagnostic logs, platform metrics).

Azure now allows users to assign different pricing plans at the table level, including the Basic plan, which offers significantly lower ingestion costs at the expense of reduced query functionality. This provides a valuable opportunity to align cost with access patterns by assigning less expensive plans to tables that are retained for record-keeping or compliance, rather than analysis.

In Kubernetes environments, resources such as ConfigMaps, Secrets, Services, and Persistent Volume Claims (PVCs) are often created dynamically by applications or deployment pipelines. When applications are removed or reconfigured, these resources may be left behind if not explicitly cleaned up. Over time, they accumulate as orphaned resources — not referenced by any live workload.

Some of these objects, like PVCs or Services of type LoadBalancer, result in active infrastructure that continues to incur cloud charges (e.g., retained EBS volumes or unused Elastic Load Balancers). Even lightweight objects like ConfigMaps and Secrets bloat the API server’s object store, causing latency and impacting deployments/scaling,, clutter the control plane, and complicate configuration management. This issue is especially common during cluster upgrades, namespace decommissioning, and workload migrations.

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.