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 As a result, outdated VM series versions continue to run indefinitely, even in environments where brief downtime is acceptable.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.
Highly compressible datasets, such as those with repeated string fields, nested structures, or uniform rows, can benefit significantly from physical storage billing. Yet most datasets remain on logical storage by default, even when physical storage would reduce costs.
This inefficiency is common for cold or infrequently updated datasets that are no longer optimized or regularly reviewed. Because storage behavior and data characteristics evolve, failing to periodically reassess the billing model may result in persistent waste.
Many organizations continue running short-lived or low-intensity SQL workloads — such as dashboards, exploratory queries, and BI tool integrations — on traditional clusters. This leads to idle compute, overprovisioning, and high baseline costs, especially when the clusters are always-on. Databricks SQL Serverless is optimized for bursty, interactive use cases with auto-scaling and pay-per-second pricing, making it better suited for this class of workloads. Failing to migrate to serverless for these patterns results in unnecessary cost without performance benefit.
Running varied workload types (e.g., ETL pipelines, ML training, SQL dashboards) on the same cluster introduces inefficiencies. Each workload has different runtime characteristics, scaling needs, and performance sensitivities. When mixed together, resource contention can degrade job performance, increase cost, and obscure cost attribution.
ETL jobs may overprovision memory, while lightweight SQL queries may trigger unnecessary cluster scale-ups. Job failures or retries may increase due to contention, and queued jobs can further inflate runtime costs. Without clear segmentation, teams lose the ability to tune environments for specific use cases or monitor workload-specific efficiency.
Autoscaling is a core mechanism for aligning compute supply with workload demand, yet it's often underutilized or misconfigured. In older clusters or ad-hoc environments, autoscaling may be disabled by default or set with tight min/max worker limits that prevent scaling. This can lead to persistent overprovisioning (and wasted cost during idle periods) or underperformance due to insufficient parallelism and job queuing. Poor autoscaling settings are especially common in manually created all-purpose clusters, where idle resources often go unnoticed.
Overly wide autoscaling ranges can also introduce instability: Databricks may rapidly scale up to the upper limit if demand briefly spikes, leading to cost spikes or degraded performance. Understanding workload characteristics is key to tuning autoscaling appropriately.
Photon is frequently enabled by default across Databricks workspaces, including for development, testing, and low-concurrency workloads. In these non-production contexts, job runtimes are typically shorter, SLAs are relaxed or nonexistent, and performance gains offer little business value.
Enabling Photon in these environments can inflate DBU costs substantially without meaningful runtime improvements. By not differentiating cluster configurations between production and non-production, organizations may pay a premium for workloads that could run just as efficiently on standard compute.
Cluster policies can be used to restrict Photon usage to explicitly tagged production workloads, helping enforce cost-conscious defaults and reduce unnecessary spend.
Many Spark and SQL workloads in Databricks suffer from micro-optimization issues — such as unfiltered joins, unnecessary shuffles, missing broadcast joins, and repeated scans of uncached data. These problems increase compute time and resource utilization, especially in exploratory or development environments. Disabling Adaptive Query Execution (AQE) can further exacerbate inefficiencies. Optimizing queries reduces DBU costs, improves cluster performance, and enhances user experience.
Amazon EMR clusters often run on large, multi-node EC2 fleets, making them costly to leave running unnecessarily. If a cluster becomes idle—no longer processing jobs—but is not terminated, it continues accruing EC2 and EMR service charges. Many teams forget to shut down clusters manually or leave them running for debugging, staging, or future job use. Without an auto-termination policy, this oversight leads to significant unnecessary spend.
Many teams run workloads on standard Fargate pricing even when the workload is fault-tolerant and could tolerate interruptions. Fargate Spot provides the same performance characteristics at up to 70% lower cost, making it ideal for stateless jobs, batch processing, CI/CD runners, or retry-friendly microservices.
If backup retention settings are too high or old automated backups are unnecessarily retained, costs can accumulate rapidly. RDS backup storage is significantly more expensive than equivalent storage in S3. For long-term retention or compliance use cases, exporting backups to S3 (e.g., via snapshot export to Amazon S3 in Parquet) is often more cost-effective than retaining them in RDS-native format.
Lambda functions default to the x86\_64 architecture, which is more expensive than Arm64. For many workloads, especially those written in interpreted languages (e.g., Python, Node.js) or compiled to architecture-neutral bytecode (e.g., Java), there is no dependency on x86-specific binaries. In such cases, moving to Arm64 can reduce compute costs by 20% without impacting functionality. Despite this, many teams continue to run Lambda functions on x86\_64 due to legacy configurations, inertia, or lack of awareness. This leads to avoidable spending, particularly at scale or in high-volume environments.
Improper design choices in AWS Step Functions can lead to unnecessary charges. For example: * Using Standard Workflows for short-lived, high-frequency executions leads to excessive per-transition charges. * Using Express Workflows for long-running processes (close to or exceeding the 5-minute limit) may cause timeouts or retries. * Inefficient use of states—such as chaining many simple states instead of combining logic into a Lambda function—can increase cost in both workflow types. * Overuse of payload-passing between states (especially in Express workflows) increases GB-second and data transfer charges.
By default, EventBridge includes retry mechanisms for delivery failures, particularly when targets like Lambda functions or Step Functions fail to process an event. However, if these retry policies are disabled or misconfigured, EventBridge may treat failed deliveries as successful, prompting upstream services to republish the same event multiple times in response to undelivered outcomes. This leads to: * Duplicate event publishing and delivery * Unnecessary compute triggered by repeated events * Increased EventBridge, downstream service, and data transfer costs This behavior is especially problematic in systems where idempotency is not strictly enforced and retries are managed externally by upstream services.
By default, CloudWatch Log Groups use the Standard log class, which applies higher rates for both ingestion and storage. AWS also offers an Infrequent Access (IA) log class designed for logs that are rarely queried — such as audit trails, debugging output, or compliance records. Many teams assume storage is the dominant cost driver in CloudWatch, but in high-volume environments, ingestion costs can account for the majority of spend. When logs that are infrequently accessed are ingested into the Standard class, it leads to unnecessary costs without impacting observability. The IA log class offers significantly reduced rates for ingestion and storage, making it a better fit for logs used primarily for post-incident review, compliance retention, or ad hoc forensic analysis.
When an EC2 Mac instance is stopped or terminated, its associated dedicated host remains allocated by default. Because Mac instances are the only EC2 type billed at the host level, charges continue to accrue as long as the host is retained. This can lead to significant waste when: * Instances are stopped but the host is never released * Hosts are retained unintentionally after workloads are migrated or decommissioned * Automation only terminates instances without deallocating hosts
In many Databricks environments, large Delta tables are created without enabling standard optimization features like partitioning and Z-Ordering. Without these, queries scanning large datasets may read far more data than necessary, increasing execution time and compute usage. * Partitioning organizes data by a specified column to reduce scan scope. * Z-Ordering optimizes file sorting to minimize I/O during range queries or filters. * Delta Format enables additional optimizations like data skipping and compaction. Failing to use these features in high-volume tables often results in avoidable performance overhead and elevated spend, especially in environments with frequent exploratory queries or BI workloads.
Business Intelligence dashboards and ad-hoc analyst queries frequently drive Databricks compute usage — especially when: * Dashboards are auto-refreshed too frequently * Queries scan full datasets instead of leveraging filtered views or materialized tables * Inefficient joins or large broadcast operations are used * Redundant or exploratory queries are triggered during interactive exploration This often results in clusters staying active for longer than necessary, or being autoscaled up to handle inefficient workloads, leading to unnecessary DBU consumption.
AWS Backup does not natively support global deduplication or change block tracking across backups. As a result, even traditional incremental or differential backup strategies (e.g., daily incremental, weekly full) can accumulate redundant data. Over time, this leads to higher-than-necessary storage usage and cost — especially in environments with frequent backup schedules or large data volumes that only change minimally between snapshots. While some third-party agents can implement CBT and deduplication at the client level, AWS Backup alone offers no built-in mechanism to avoid storing unchanged data across backup generations.
Retry storms occur when a function fails and is automatically retried repeatedly due to default retry behavior for asynchronous events (e.g., SQS, EventBridge). If the error is persistent and unhandled, retries can accumulate rapidly — often invisibly — creating a large volume of billable executions with no successful outcome. This is especially costly when functions run for extended durations or have high memory allocation.
Bigtable automatically splits data into tablets (shards), which are distributed across provisioned nodes. However, poorly designed row key schemas or excessive shard counts (caused by high cardinality, hash-based keys, or timestamp-first designs) can result in performance bottlenecks or hot spotting. To compensate, users often scale up node counts — increasing costs — when the real issue lies in suboptimal data distribution. This leads to inflated infrastructure spend without actual workload increase.
Memorystore instances that are provisioned but unused — whether due to deprecated services, orphaned environments, or development/testing phases ending — continue to incur memory and infrastructure charges. Because usage-based metrics like client connections or cache hit ratios are not tied to billing, an idle instance costs the same as a heavily used one. This makes it critical to identify and decommission inactive caches.
Cloud SQL instances are often over-provisioned or left running despite low utilization. Since billing is based on allocated vCPUs, memory, and storage — not usage — any misalignment between actual workload needs and provisioned capacity leads to unnecessary spend. Common causes include: * Initial oversizing during launch that was never revisited * Non-production environments with continuous uptime but minimal use * Databases used intermittently (e.g., for nightly reports) but kept running 24/7 Without rightsizing or scheduling strategies, these instances generate ongoing cost with limited business value.
In Cloud Run, each revision is deployed with a fixed memory allocation (e.g., 512MiB, 1GiB, 2GiB, etc.). These settings are often overestimated during initial development or copied from templates. Unlike auto-scaling platforms that adapt instance size based on workload, Cloud Run continues to bill per the allocated amount regardless of actual memory used during execution. If a service consistently uses significantly less memory than allocated, it results in avoidable overpayment per request — especially for high-throughput or long-running services. Since memory and CPU are billed together based on configured values, this inefficiency compounds quickly at scale.
Each Cloud NAT gateway provisioned in GCP incurs hourly charges for each external IP address attached, regardless of whether traffic is flowing through the gateway. In many environments, NAT configurations are created for temporary access (e.g., one-off updates, patching windows, or ephemeral resources) and are never cleaned up. If no traffic is flowing, these NAT gateways remain idle yet continue to generate charges due to reserved IPs and persistent gateway configuration. This is especially common in non-production environments or when legacy configurations are forgotten.
Node pools provisioned with large or specialized VMs (e.g., high-memory, GPU-enabled, or compute-optimized) can be significantly overprovisioned relative to the actual pod requirements. If workloads consistently leave a large portion of resources unused (e.g., low CPU/memory request-to-capacity ratio), the organization incurs unnecessary compute spend. This often happens in early cluster design phases, after application demand shifts, or when teams allocate for peak usage without autoscaling.
Cloud Memorystore instances that remain idle—i.e., not receiving read or write requests—continue to incur full costs based on provisioned size. In test environments, migration scenarios, or deprecated application components, Redis instances are often left running unintentionally. Since Redis does not autoscale or suspend, unused capacity results in 100% waste until explicitly deleted.
By default, Cloud Logging retains logs for 30 days. However, many organizations increase retention to 90 days, 365 days, or longer — even for non-critical logs such as debug-level messages, transient system logs, or audit logs in dev environments. This extended retention can lead to unnecessary costs, especially when: * Logs are never queried after the first few days * Observability tooling duplicates logs elsewhere (e.g., SIEM platforms) * Retention settings are applied globally without considering log type or project criticality
Pub/Sub Lite is a cost-effective alternative to standard Pub/Sub, but it requires explicitly provisioning throughput capacity. When publish or subscribe throughput is overestimated, customers continue to pay for unused capacity — similar to idle virtual machines or overprovisioned IOPS. This inefficiency is often found in development environments or early-stage production workloads where traffic patterns are unpredictable or have since decreased.
Even when no user workloads are active, GKE Autopilot clusters continue running system-managed pods that accrue compute and storage charges. These include control plane components and built-in agents for observability and networking. If Autopilot clusters are deployed in non-production or experimental environments and left idle, they may silently accrue ongoing charges unrelated to application activity. This inefficiency often occurs in: * Dev/test clusters that are spun up temporarily but not deleted * Clusters used for one-time jobs or training workloads * Scheduled workloads that run infrequently but don't trigger downscaling
When GCS object versioning is enabled, every overwrite or delete operation creates a new noncurrent version. Without a lifecycle rule to manage old versions, they persist indefinitely. Over time, this results in: * Accumulation of outdated data * Unnecessary storage costs, especially in Standard or Nearline classes * Lack of visibility into what is still needed vs. legacy debris This issue often goes unnoticed in environments with frequent data updates or automated processes (e.g., logs, models, config snapshots).
If a table is not partitioned by a relevant column (typically a timestamp), every query scans the entire dataset, even if filtering by date. This leads to: * High costs per query * Long execution times * Inefficient use of resources when querying recent or small subsets of data This inefficiency is especially common in: * Event or log data stored in raw, unpartitioned form Historical data migrations without schema optimization * Workloads developed without awareness of BigQuery’s scanning model
Cloud Run allows users to allocate up to 8 GB of memory per container instance. If memory is overestimated — often as a buffer or based on unvalidated assumptions — customers pay for more than what the workload consumes during execution. Unlike in VM-based environments where memory might be shared or underutilized without direct cost impact, in Cloud Run, you're billed precisely for what you allocate. This inefficiency often results from: * Defaulting to high memory values for “safety” * Not using monitoring tools to assess actual memory usage * Lack of clear ownership over service tuning
Teams often adopt flat-rate pricing (slot reservations) to stabilize costs or optimize for heavy, recurring workloads. However, if query volumes drop — due to seasonal cycles, architectural shifts (e.g., workload migration), or inaccurate forecasting — those reserved slots may sit underused. This inefficiency is easy to miss, as the cost remains fixed and detached from usage volume. Unlike autoscaling models, reservations require active monitoring and manual adjustment. In some organizations, multiple projects reserve separate slot pools, exacerbating waste through fragmentation.
Cloud Functions scale to zero when idle. When invoked after inactivity, they undergo a "cold start," initializing runtime, loading dependencies, and establishing any required network connections (e.g., VPC connectors). These cold starts can dramatically increase execution time, especially for functions with: * High memory allocations * Heavy initialization logic * VPC connector requirements If cold starts are frequent, customers may be paying for unnecessary compute time — particularly in latency-sensitive workloads — without receiving proportional value.
When EC2 instances are provisioned, each attached EBS volume has a `DeleteOnTermination` flag that determines whether it will be deleted when the instance is terminated. If this flag is set to `false` — often unintentionally in custom launch templates, AMIs, or older automation scripts — volumes persist after termination, resulting in orphaned storage. While detached volumes are easy to detect and clean up after the fact, proactively identifying attached volumes with `DeleteOnTermination=false` can prevent future waste before it occurs.
While moving objects to colder storage classes like Glacier or Infrequent Access (IA) can reduce storage costs, premature transitions without analyzing historical access patterns can lead to unintended expenses. Retrieval charges, restore time delays, and early delete penalties often go unaccounted for in simplistic tiering decisions. This inefficiency arises when teams default to colder tiers based solely on perceived “age” of data or storage savings—without confirming access frequency, restore time SLAs, or application requirements. Unlike inefficiencies focused on *underuse* of cold storage, this inefficiency reflects *overuse* or misalignment, resulting in higher total costs or operational friction.
VM-based Committed Use Discounts in GCP offer cost savings for predictable workloads, but they are rigid: they apply only to specified VM types, quantities, and regions. When organizations evolve their architecture — such as moving to GKE (Kubernetes), Cloud Run, or autoscaling — usage patterns often shift away from the original commitments. Because GCP lacks flexible reallocation options like AWS Convertible RIs or Savings Plans, underutilized commitments lead to sustained, silent waste. This is especially common when workload changes go uncoordinated with finance or centralized planning.
Many environments continue using io1 volumes for high-performance workloads due to legacy provisioning or lack of awareness of io2 benefits. io2 volumes provide equivalent or better performance and durability with reduced cost at scale. Failing to adopt io2 where appropriate results in unnecessary spend on IOPS-heavy volumes.
When large numbers of objects are deleted from S3—such as during cleanup or lifecycle transitions—CloudTrail can log every individual delete operation if data event logging is enabled. This is especially costly when deleting millions of objects from buckets configured with CloudTrail data event logging at the object level. The resulting volume of logs can cause a significant, unexpected spike in CloudTrail charges, sometimes exceeding the cost of the underlying S3 operations themselves. This inefficiency often occurs when teams initiate bulk deletions for cleanup or cost savings without realizing that CloudTrail logs every API call, including `DeleteObject`, if data event logging is active for the bucket.
Non-production OpenSearch domains often inherit Multi-AZ configurations from production setups without clear justification. This leads to redundant replica shards across AZs, inflating both compute and storage costs. Unless strict uptime or fault tolerance requirements exist, most dev/test workloads do not benefit from Multi-AZ redundancy.
Recursive invocation occurs when a Lambda function triggers itself directly or indirectly, often through an event source like SQS, SNS, or another Lambda. This loop can be unintentional — for example, when the function writes output to a queue it also consumes. Without controls, this can lead to runaway invocations, dramatically increasing cost with no business value.
In non-production environments, enabling Multi-AZ Redis clusters introduces redundant replicas that may not deliver meaningful business value. These replicas are often kept in sync across Availability Zones, incurring both compute and inter-AZ data transfer costs. For development or test clusters that can tolerate occasional downtime or data loss, a single-AZ deployment is typically sufficient and significantly less expensive.
RDS Multi-AZ deployments are designed for production-grade fault tolerance. In non-production environments, this configuration doubles the cost of database instances and storage with little added value. Unless explicitly required for high-availability testing, Multi-AZ in dev, staging, or test environments typically results in avoidable expense.
Multi-AZ deployment is often essential for production workloads, but its use in non-production environments (e.g., development, test, QA) offers minimal value. These environments typically do not require high availability, yet still incur the full cost of redundant compute, storage, and data transfer. This results in unnecessary spend without operational benefit.
When a Lambda function processes messages from an SQS queue but fails to handle certain messages properly, the same messages may be returned to the queue and retried repeatedly. In some cases, especially if the Lambda is also writing messages back to the same or a chained queue, this can create a recursive invocation loop. This loop results in high invocation counts, prolonged execution, and unnecessary costs, particularly if retries continue without a termination strategy.
RDS reader nodes are intended to handle read-only workloads, allowing for traffic offloading from the primary (writer) node. However, in many environments, services are misconfigured or hardcoded to send all traffic—including reads—to the writer node. This results in underutilized reader nodes that still incur full hourly charges, while the writer node becomes a performance bottleneck and may require upsizing to handle unnecessary load. This inefficiency reduces cost-effectiveness and resilience, especially in high-throughput or scalable architectures.
Provisioned load balancers continue to generate costs even when they are no longer serving meaningful traffic. This often occurs when applications are decommissioned, testing infrastructure is left behind, or backend services are removed without deleting the associated frontend configurations. Without ingress or egress traffic, these load balancers offer no functional value but still consume billable resources, including forwarding rules and reserved external IPs.
Load balancers incur charges based on provisioned bandwidth shape, even if backend traffic is minimal. If traffic is low, or if only one backend server is configured, the load balancer may be oversized or unnecessary, especially in test or staging environments.
Without lifecycle policies, data in OCI Object Storage remains in the default storage tier indefinitely—even if it is rarely accessed. This can lead to growing costs from unneeded or rarely accessed data that could be expired or transitioned to lower-cost tiers like Archive Storage.
Block volumes that are not attached to any instance continue to incur charges. These often accumulate after instance deletion or reconfiguration. Unlike boot volumes, unattached data volumes may be harder to track if not labeled or tagged clearly.
OCI Object Storage buckets accrue charges based on data volume stored, even if no activity has occurred. Buckets that haven't been read from or written to in months may contain outdated data or artifacts from discontinued projects.
OCI Compute instances incur cost based on provisioned CPU and memory, even when the instance is lightly loaded. Instances that show consistently low usage across time, such as those used only for occasional tasks, test environments, or forgotten workloads, may be overprovisioned relative to their actual needs.
When a Compute instance is terminated in OCI, the associated boot volume is not deleted by default. If the termination settings don’t explicitly delete the boot volume, it persists and continues to generate storage charges. Because boot volumes are managed under the Block Volumes service, not within the Compute UI, they’re easy to overlook—especially in environments with frequent provisioning and teardown. Over time, these orphaned boot volumes can accumulate and contribute to unnecessary costs.
If an AWS WorkSpace has been provisioned but not accessed in a meaningful timeframe, it may represent waste—particularly if it is set to monthly billing. Many organizations leave WorkSpaces active for users who no longer need them or have shifted roles, leading to persistent charges without corresponding business value. Even in hourly mode, costs can accrue if WorkSpaces are left in a running state.
MemoryDB now supports Valkey, a drop-in replacement for Redis OSS offering significant cost and performance advantages. However, many deployments still default to Redis OSS, incurring higher hourly costs and unnecessary data write charges. For compatible workloads, continuing to use Redis OSS instead of Valkey represents a missed opportunity for savings and modernization.
When replicating an EFS file system across AWS regions (e.g., for disaster recovery), the destination file system does not automatically inherit the source’s lifecycle policy. As a result, files replicated to the destination will remain in the Standard storage class unless a new lifecycle policy is explicitly configured. Over time, this can lead to significantly higher storage costs, particularly in DR environments where data is rarely accessed but still replicated in full.
EFS offers lifecycle policies that transition files from the Standard tier to Infrequent Access (IA) based on inactivity, significantly reducing storage costs for cold data. When this feature is not enabled, infrequently accessed files remain in the more expensive Standard tier indefinitely. This often occurs when the file system is initially provisioned for performance but long-term access patterns are not reevaluated.
Storing raw JSON or CSV files in S3—especially when written frequently in small batches—leads to excessive scan costs in Athena. These formats are row-based and verbose, requiring Athena to scan and parse the full content even when only a few fields are queried. Without columnar formats, partitioning, or metadata-aware table formats, queries become inefficient and expensive, especially in high-volume environments.
An ELB with only one registered EC2 instance does not achieve its core purpose—distributing traffic across multiple backends. In this configuration, the ELB adds complexity and cost without improving availability, scalability, or fault tolerance. This setup is often the result of premature scaling design or misunderstood architecture patterns. If there's no plan to horizontally scale the application, the ELB can often be removed entirely without user impact.
EBS volumes often remain significantly overprovisioned compared to the actual data stored on them. Because billing is based on the total provisioned capacity—not actual usage—this creates ongoing waste when large volumes are only partially used. Overprovisioning may result from default sizing in templates, misestimated requirements, or conservative provisioning practices. Identifying and remediating these cases can lead to meaningful storage cost reductions without impacting workload performance.
Photon is enabled by default on many Databricks compute configurations. While it can accelerate certain SQL and DataFrame operations, its performance benefits are workload-specific and may not justify the increased DBU cost. Many pipelines, particularly ETL jobs or simpler Spark workloads, do not benefit materially from Photon but still incur the higher DBU multiplier. Disabling Photon by default and allowing it only where proven beneficial can reduce cost without degrading performance.
When fleet auto scaling policies maintain more active instances than are required to support current usage—particularly during off-peak hours—organizations incur unnecessary compute costs. Fleets often remain oversized due to conservative default configurations or lack of schedule-based scaling. Tuning the scaling policies to better reflect usage patterns ensures that streaming infrastructure aligns with actual demand.
AppStream fleets often default to instance types designed for worst-case or peak usage scenarios, even when average workloads are significantly lighter. This leads to consistently low utilization of CPU, memory, or GPU resources and inflated infrastructure costs. By right-sizing AppStream instances based on actual workload needs, organizations can reduce spend without compromising user experience.
When AppStream builder instances are left running but unused, they continue to generate compute charges without delivering any value. These instances are commonly left active after configuration or image creation is completed but can be safely stopped or terminated when not in use. Identifying and decommissioning inactive builders helps reduce unnecessary compute costs.
EBS Snapshot Archive is a lower-cost storage tier for rarely accessed snapshots retained for compliance, regulatory, or long-term backup purposes. Archiving snapshots that do not require frequent or fast retrieval can reduce snapshot storage costs by up to 75%. Despite this, many organizations retain large volumes of snapshots in the standard tier long after their operational value has expired.
When reservations are scoped only to a single subscription, any unused capacity cannot be applied to matching resources in other subscriptions within the same tenant. This leads to underutilization of the committed reservation and continued on-demand charges in other parts of the organization. Enabling Shared scope allows all eligible subscriptions to consume the reservation benefit, improving utilization and reducing overall spend. This is particularly impactful in environments with decentralized provisioning, such as across dev/test/prod subscriptions or multiple business units.
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.
Azure SQL Database resources are frequently overprovisioned due to default configurations, conservative sizing, or legacy requirements that no longer apply. This inefficiency appears across all deployment models:
* Single Databases may be assigned more DTUs or vCores than the workload requires * Elastic Pools may be oversized for the actual demand of pooled databases * Managed Instances are often deployed with excess compute capacity that remains underutilized
Because billing is based on provisioned capacity, not actual consumption, organizations incur unnecessary costs when sizing is not aligned with workload behavior. Without regular reviews, these resources become persistent sources of waste — especially across dev/test environments or variable workloads.
Azure Database for PostgreSQL – Flexible Server often defaults to general-purpose D-series VMs, which may be oversized for many production or development workloads. PostgreSQL typically does not require a high sustained high CPU, making it well-suited to memory-optimized (E-series) or burstable (B-series) instances.
When actual usage consistently falls below the provisioned capacity — particularly CPU — the deployment may be overprovisioned, resulting in unnecessary compute charges. Choosing the wrong VM family or size leads to silent overspend, especially in long-lived database environments.
Azure SQL deployments often reserve more storage than needed, either due to default provisioning settings or anticipated future growth. Over time, if actual usage remains low, these oversized allocations generate unnecessary storage costs.
In Elastic Pools, resizing can be done through standard configuration updates. In Managed Instances, reducing storage may require a shrink operation to reclaim unused space before reallocation is permitted. Without regular review, these overprovisioned environments persist as silent cost contributors.
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, enhanced networking options, and eligibility for commitment-based discounts.
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 and missing available savings.
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.
An Azure SQL Elastic Pool continues to incur costs even if it contains no databases. This can occur when databases are deleted, migrated to single-instance configurations, or consolidated elsewhere — but the pool itself remains provisioned. In such cases, the pool becomes an idle resource consuming budget without delivering value.
This inefficiency often goes undetected in large or decentralized environments where cleanup workflows are manual or inconsistent. Since the pool reserves compute and storage regardless of usage, it silently contributes to unnecessary spend over time.
When EC2 instances within a VPC access Amazon S3 in the same region without a Gateway VPC Endpoint, traffic is routed through the public S3 endpoint and incurs standard internet egress charges — even though it remains within the AWS network. This results in unnecessary egress charges, as AWS treats this traffic as data transfer out to the internet, billed under the S3 service.
By contrast, provisioning a Gateway Endpoint for S3 allows traffic between EC2 and S3 to flow over the AWS private backbone at no additional cost. This configuration is especially important for data-intensive applications, such as analytics jobs, backups, or frequent uploads/downloads, where the cumulative data transfer can be substantial.
Because the egress cost is billed under S3, it is often misattributed or overlooked during EC2 or networking reviews, leading to silent overspend.
NAT Gateways are designed to serve private subnets within the same Availability Zone. When subnets in one AZ are configured to route traffic through a NAT Gateway in a different AZ, the traffic crosses AZ boundaries and incurs inter-AZ data transfer charges in addition to the standard NAT processing fees.
This typically happens when:
* NAT Gateways are deployed in multiple AZs (as recommended for resilience), but * Route tables for all subnets are configured to send traffic to a single NAT Gateway, ignoring AZ placement
In high-throughput environments, this misalignment silently generates excess cost. Ensuring that each subnet routes through the NAT Gateway in its own AZ avoids inter-AZ charges and aligns with AWS architectural best practices.
Search Optimization can enable significant cost savings when selectively applied to workloads that heavily rely on point-lookup queries. By improving lookup efficiency, it allows smaller warehouses to satisfy performance SLAs, reducing credit consumption.
However, inefficiencies arise when:
Regular review of query patterns and warehouse sizing is essential to maximize the intended benefit of Search Optimization.
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.
RDS workloads often evolve — changing engine types, rightsizing instances, or shifting to Aurora or serverless models. When these changes occur after Reserved Instances have been purchased, the existing commitments may no longer match active usage. This results in silent overspend, as underutilized RIs continue billing without offsetting usage.
Unlike Convertible EC2 RIs, RDS RIs cannot be exchanged. Selling unused RDS RIs is not supported. In rare cases, AWS Support may approve a goodwill adjustment, but this is not guaranteed. The most effective way to recover value is to steer eligible workloads back toward the reserved configuration.
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.
Inefficient pipeline refresh scheduling occurs when data refresh operations are executed more frequently, or with more compute resources, than the actual downstream business usage requires.
Without aligning refresh frequency and resource allocation to true data consumption patterns (e.g., report access rates in Tableau or Sigma), organizations can waste substantial Snowflake credits maintaining underutilized or rarely accessed data assets.
Inefficiency arises when MVs are either underused or misused.
Proper evaluation of workload patterns and strategic use of MVs is critical to achieve a net cost benefit.
Organizations may experience unnecessary Snowflake spend due to inefficient query-to-warehouse routing, lack of dynamic warehouse scaling, or failure to consolidate workloads during low-usage periods. Third-party platforms offer solutions to address these inefficiencies:
Choosing between these solutions depends heavily on the organization's internal capabilities and desired balance between control and automation.
Excessive Auto-Clustering costs occur when tables experience frequent and large-scale modifications ("high churn"), causing Snowflake to constantly recluster data. This leads to significant and often hidden compute consumption for maintenance tasks, especially when table structures or loading patterns are not optimized. Poor clustering key choices, unordered data loads, or frequent full-table replacements are common drivers of unnecessary Auto-Clustering activity.
Retention of stale data occurs when old, no longer needed records are preserved within active Snowflake tables. Without lifecycle policies or regular purging, tables accumulate outdated data.
Because Snowflake’s compute charges are tied to how much data is scanned, retaining large volumes of inactive or irrelevant data can drive up both storage and query execution costs unnecessarily.
Ingesting a large number of small files (e.g., files smaller than 10 MB) using Snowpipe can lead to disproportionately high costs due to the per-file overhead charges. Each file, regardless of its size, incurs the same overhead fee, making the ingestion of numerous small files less cost-effective. Additionally, small files can increase the load on Snowflake's metadata and ingestion infrastructure, potentially impacting performance.
Snowflake automatically maintains previous versions of data when tables are modified or deleted. For tables with high churn—meaning frequent INSERT, UPDATE, DELETE, or MERGE operations—this can cause a significant buildup of historical snapshot data, even if the active data size remains small.
This hidden accumulation leads to elevated storage costs, particularly when Time Travel retention periods are long and data change rates are high. Often, teams are unaware of how much snapshot data is being stored behind the scenes.
