When Kubernetes workloads request more CPU and memory than they actually consume, nodes must reserve capacity that remains unused. This leads to lower node density, forcing the cluster to maintain more instances than necessary. Aligning resource requests with observed utilization improves cluster efficiency and reduces compute spend without sacrificing application performance.
Databricks Serverless Compute is now available for jobs and notebooks, offering a simplified, autoscaled compute environment that eliminates cluster provisioning, reduces idle overhead, and improves Spot survivability. For short-running, bursty, or interactive workloads, Serverless can significantly reduce cost by billing only for execution time. However, Serverless is not universally available or compatible with all workload types and libraries. Organizations that exclusively rely on traditional clusters may be missing emerging opportunities to reduce spend and simplify operations by leveraging Serverless where appropriate.
When EC2 usage declines, shifts to different instance families, or moves to other services (e.g., containers or serverless), organizations may find that previously purchased Standard Reserved Instances or Savings Plans no longer match current workload patterns.
This misalignment results in underutilized commitments—where costs are still incurred, but no usage is benefiting from the associated discounts. Since these commitments cannot be easily exchanged, refunded, or sold (except for eligible RIs on the RI Marketplace), the only viable path to recoup value is to steer workloads back toward the covered usage profile.
As workloads evolve, Azure Reserved Instances (RIs) may no longer align with actual usage — due to refactoring, region changes, autoscaling, or instance-type drift. When this happens, the committed usage goes unused, while new workloads run on non-covered SKUs, resulting in both underutilized reservations and full-price on-demand charges elsewhere.
The root inefficiency is architectural or operational drift away from what was originally committed — often due to team autonomy, poor RI governance, or legacy commitments. This leads to silent waste unless workloads are re-aligned to match existing reservations.
Applications running on App Service V2 plans may incur higher operational costs and degraded performance compared to V3 plans. V2 uses older hardware generations that lack access to platform-level enhancements introduced in V3, including improved cold start times, faster scaling, and enhanced networking options.
This inefficiency often arises from legacy deployments or default provisioning choices that haven't been revisited. Without proactive review, teams may continue running production workloads on suboptimal infrastructure—paying more for less performance.
App Service Plans continue to incur charges even when no applications are deployed. This can occur when applications are deleted, migrated, or retired, but the associated App Service Plan remains active. Without ongoing workloads, these idle plans become silent cost contributors — especially in higher-cost SKUs like Premium v3 or Isolated v2.
In large or decentralized environments, unused plans can accumulate quickly if cleanup is not automated or routinely enforced. These idle plans offer no functional value but continue to consume compute resources and generate operational expense.
Convertible Reserved Instances provide valuable pricing flexibility — but that flexibility is often underused. When EC2 workloads shift across instance families or OS types, the original RI may no longer apply to active usage. If the RI is not converted, the customer continues paying for unused commitment despite having the ability to adapt it.
Because conversion is a manual process and requires matching or exceeding the original RI’s value, many organizations fail to optimize their coverage. Over time, this leads to growing pools of ineffective RIs that could have been aligned to real workloads.
Many organizations default to Intel-based EC2 instances due to familiarity or assumptions about workload compatibility. However, AWS offers AMD and Graviton-based alternatives that often deliver significantly better price-performance for general-purpose and compute-optimized workloads.
By not testing workloads across available architectures, teams may continue paying a premium for Intel instances even when no specific performance or compatibility benefit exists. Over time, this results in unnecessary compute spend across development, staging, and even production environments.
Underutilized Snowflake warehouses occur when a workload is assigned a larger warehouse size than necessary. For example, a workload that could efficiently execute on a Medium (M) warehouse may be running on a Large (L) or Extra Large (XL) warehouse.This leads to unnecessary credit consumption without a proportional benefit to performance. Underutilization is often driven by early provisioning decisions that were not later reassessed, or by a desire for marginal speed improvements that do not justify the increased operational cost.
Inefficient execution of repeated queries occurs when common query patterns are frequently executed without optimization. Even if individual executions are successful, repeated inefficiencies compound overall compute consumption and credit costs.
By analyzing Snowflake's parameterized query metrics, organizations can identify top repeated queries and optimize them for better performance, resource usage, and cost-efficiency.
