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Unused EBS Volume Attached to a Stopped EC2 Instance
Storage
Cloud Provider
AWS
Service Name
Amazon Elastic Block Store (EBS)
Inefficiency Type
Unused Resource

This inefficiency occurs when an EC2 instance is stopped but still has one or more attached EBS volumes. Although the compute resource is not generating charges while stopped, the attached volumes continue to incur full storage and performance-related costs. These volumes are often overlooked in cost reviews, especially if the instance is temporarily paused or has been left in a stopped state long-term. Without regular validation, these volumes may represent unused capacity that delivers no value.

Underutilized Write Capacity on a DynamoDB Table
Databases
Cloud Provider
AWS
Service Name
Amazon DynamoDB
Inefficiency Type
Underutilization

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.

Underutilized Snowflake Warehouse
Compute
Cloud Provider
Snowflake
Service Name
Virtual Warehouse
Inefficiency Type
Underutilized Resource

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.

Underutilized Read Capacity on a DynamoDB Table
Databases
Cloud Provider
AWS
Service Name
Amazon DynamoDB
Inefficiency Type
Underutilization

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.

Underutilized RDS Instance
Databases
Cloud Provider
AWS
Service Name
Amazon RDS
Inefficiency Type
Overprovisioned Resource

This inefficiency occurs when an RDS instance is consistently operating below its provisioned capacity—for example, showing low CPU, or memory utilization over an extended period. This often results from conservative initial sizing, decreased workload demand, or failure to review and adjust after deployment. Running oversized RDS instances leads to unnecessary compute and licensing costs without delivering additional value.

Underutilized Provisioned IOPS on an EBS Volume
Storage
Cloud Provider
AWS
Service Name
Amazon Elastic Block Store (EBS)
Inefficiency Type
Overprovisioned Resource

This inefficiency occurs when an EBS volume has provisioned IOPS levels that consistently exceed the actual I/O requirements of the workload it supports. This can happen when performance buffers are estimated too high, usage patterns change over time, or default settings are left unadjusted. Provisioned IOPS above the included baseline generate ongoing charges that may not reflect actual utilization, resulting in avoidable cost.

Underutilized Instances in EC2 Auto Scaling Group
Compute
Cloud Provider
AWS
Service Name
Amazon Elastic Compute Cloud (EC2)
Inefficiency Type
Underutilized Resource

Oversized instances within Auto Scaling Groups lead to inflated baseline costs, even when scaling adjusts the number of instances dynamically. When workloads consistently use only a fraction of the available CPU, memory, or network capacity, there is an opportunity to downsize to smaller, less expensive instance types without sacrificing performance. Right-sizing helps balance capacity and efficiency, reducing compute spend while preserving workload stability.

Underutilized Kubernetes Workload
Compute
Cloud Provider
AWS
Service Name
Amazon Elastic Kubernetes Service (EKS)
Inefficiency Type
Underutilization

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.

Underutilized Azure Virtual Machine
Compute
Cloud Provider
Azure
Service Name
Azure Virtual Machines
Inefficiency Type
Overprovisioned Resource

Azure VMs are frequently provisioned with more vCPU and memory than needed, often based on template defaults or peak demand assumptions. When a VM operates well below its capacity for an extended period, it presents an opportunity to reduce costs through rightsizing. Without regular usage reviews, these inefficiencies can persist indefinitely.

Underutilized GCP VM Instance
Compute
Cloud Provider
GCP
Service Name
Compute Engine
Inefficiency Type
Overprovisioned Resource

GCP VM instances are often provisioned with more CPU or memory than needed, especially when using custom machine types or legacy templates. If an instance consistently consumes only a small portion of its allocated resources, it likely represents an opportunity to reduce costs through rightsizing. Without proactive reviews, these oversized instances can remain unnoticed and continue to incur unnecessary charges.

There are no inefficiency matches the current filters.