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.

Provisioned capacity mode is appropriate for workloads with consistent or predictable throughput. However, when write capacity is significantly over-provisioned relative to actual usage, it results in wasted spend. This inefficiency is especially common in dev/test environments, legacy systems, or workloads that have tapered off over time but were never adjusted.

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.

Provisioned capacity mode is appropriate for workloads with consistent or predictable throughput. However, when read capacity is significantly over-provisioned relative to actual usage, it results in wasted spend. This inefficiency is especially common in dev/test environments, legacy systems, or workloads that have tapered off over time but were never adjusted.

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.

Azure Files Standard tier is cost-effective for low-traffic scenarios but imposes per-operation charges that grow rapidly with frequent access. In contrast, Premium tier provides consistent IOPS and throughput without additional transaction charges. When high-throughput or performance-sensitive workloads (e.g., real-time application data, logs, user file interactions) are placed in the Standard tier, transaction costs can significantly exceed expectations.

This inefficiency occurs when teams prioritize low storage cost without considering IOPS or throughput needs, or when workloads grow more active over time without reevaluation of their storage configuration. Unlike Blob Storage, migrating to Azure Files Premium requires creating a new storage account, making this an often-overlooked optimization.

Azure Blob Storage tiers are designed to optimize cost based on access frequency. However, when frequently accessed data is stored in the Cool or Archive tiers—either due to misconfiguration, default settings, or cost-only optimization—transaction costs can spike. These tiers impose significantly higher charges for read/write operations and metadata access compared to the Hot tier.

This misalignment is common in analytics, backup, and log-processing scenarios where large volumes of object-level operations occur regularly. While the per-GB storage rate is lower, the overall cost becomes higher due to frequent access. This inefficiency is silent but accumulates rapidly in active workloads.

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.

Azure users may enable the SFTP feature on Storage Accounts during migration tests, integration scenarios, or experimentation. However, if left enabled after initial use, the feature continues to generate flat hourly charges — even when no SFTP traffic occurs.

Because this fee is incurred silently and independently of storage usage, it often goes unnoticed in cost reviews. When SFTP is not actively used for data ingestion or export, disabling it can eliminate unnecessary charges without impacting other access methods.

Azure SQL databases often use the default backup configuration, which stores backups in RA-GRS storage to ensure geo-redundancy. While suitable for high-availability production systems, this level of resilience may be unnecessary for development, testing, or lower-impact workloads.

Using RA-GRS without a business requirement results in avoidable costs. Downgrading to LRS or ZRS — where appropriate — can significantly reduce monthly backup storage spend. This change has no impact on backup frequency or retention behavior, only the underlying storage replication method.