Asset Library / FinOps Use Cases / Compare resource usage month over month
FinOps Practitioners can compare usage and costs across charge periods to identify changes in usage month over month.
SELECT MONTH(ChargePeriodStart), ProviderName, ServiceName, ResourceId, SkuId, ConsumedUnit, SUM(ConsumedQuantity) AS TotalConsumedQuantity FROM focus_data_table WHERE ChargeCategory='Usage' AND ChargePeriodStart >= ? and ChargePeriodEnd < ? GROUP BY MONTH(ChargePeriodStart), ProviderName, ServiceName, ResourceId, SkuId, ConsumedUnit
Measures the unpredicted variance of effective cost associated with CSP Cloud usage recorded over a given period of time.
Total effective cost associated with all anomalies events detected less the predicted spend of the services related to the identified anomalies.
Data Sources:
Related Capabilities
Measures the practice of adhering to predefined schedules, e.g. automated starting, stopping, or modifying the power state of virtual machines (VMs) or other cloud resources.
CSPs offer the ability to control the power state of these resources, allowing users to conserve resources, reduce costs, and optimize performance by dynamically adjusting their availability based on demand.
Power schedule adherence percentage per month = (Total number of hours the system is required to run/Total number of hours the system is running)
Note: A higher percentage means better adherence.
Data Sources:
Measure of cloud compute resource utilization, e.g. Compute (EC2), Block Storage (EBS), or Object Storage (S3).
For each resource type, calculate the amount of resources utilized as a percentage of the the total capacity allocated. Resource utilization rates should be considered over time, and as compared to the expected utilization based on the workloads running. Practitioners should also consider average vs. peak utilization rates.
Compute: CPU Utilization Rate (total consumed quantity in core-hrs or cpu-hrs / Total CPUs Allocated), Memory Utilization Rate (Total Usage in GB / Total Memory Allocated)
Volumes: Throughput Utilized / Throughput Provisioned, IOPS Utilized / IOPS provisioned, Storage Utilized / Storage Provisioned (per volume)
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Related Capabilities
Related Assets
Workload Optimization Library
A collection of resources to help identify types of cloud cost waste by service provider, including links to additional tools.
How to Optimize Cloud Usage
Use this paper to better understand what usage optimization is and what to consider if your organization wants to maximize the value of the cloud.
Measure of the percentage of resources running on ‘modern’ types (e.g. instance types, volume type).
In general, cloud service providers come out with modern resource types that often come with improved price/performance.
NOTE: Always compare the actual cost impact of a migration vs. level of effort required. Not all modernizations will result in cost savings. Always consider your active reservations before migrating workloads.
Number of instances on ‘legacy’ resource types / total number of instances (can be done by app, account, total)
Data Sources:
Measure of the percent of object storage stored on a Frequent Access Tier.
The ideal percentage is not standard across all cloud environments, but measuring the percentage of total storage in a frequent access tier (ex: S3 Standard) can help organizations spot-check. Storage costs consist of a tradeoff between persistent storage costs vs. access and data transfer fees. Many companies can host 50%+ (sometimes, much higher!) in infrequent or “cold” storage (ie.g. AWS Glacier).
Number of GB in Standard (or “frequently accessed” tiers vs. total GBs stored)
Data Sources:
Measure of unused cloud resources, e.g., unattached/orphaned storage volumes, load balancers, EIPS, Network gateways, snapshots.
Volumes: Pricing Quantity of Unattached Volumes / Total Number of Volumes
Number of Snapshots not accessed over a defined period of time / total # of Snapshots
EIPS: Quantity of unattached Elastic IPs / Total Elastic IP’s
Data Sources:
Related Capabilities
Related Assets
Workload Optimization Library
A collection of resources to help identify types of cloud cost waste by service provider, including links to additional tools.
Rightsizing Virtual Machines on Azure
Use this Playbook for guidance on reducing waste when rightsizing VMs when using Microsoft Azure.
How to Build and Optimize FinOps Data Workflows
Use this Paper to learn how to build FinOps cloud cost and usage data ingestion, hygiene, and normalization practices.
How to Optimize Cloud Usage
Use this paper to better understand what usage optimization is and what to consider if your organization wants to maximize the value of the cloud.
Measures effectiveness of an auto-scaling system.
The goal of auto-scaling efficiency is to ensure that the right amount of resources are provisioned and de-provisioned in response to changes in demand, in order to achieve a balance between performance, cost, and resource utilization.
Auto-scaling efficiency rate = Maximum capacity cost of running workload to meet workload demand / Cost of running workload with auto-scaling to meet same workload demand. The higher the efficiency rate the more effective the auto-scaling is. Effective Cost can be used in this formula, or the List Cost metric can be used to eliminate the effect of discounts and focus entirely on the scaling effect.
Data Sources: CSP Billing Data
Related Capabilities
Related Assets
Cost Optimization for AWS EC2 Autoscaling
Use this Playbook to learn how to find ways to reduce waste with AWS EC2 Autoscaling.
Architecting VM-based Applications for Cost Efficiency
Use this guidance to inform how your engineering teams architect VM-based applications with a focus cloud cost efficiency.
Why Architecting Databases for Cost Efficiency Matters
Learn key FinOps and cost-conscious considerations and decision-making when architecting cloud database services at scale.
Measures amount of cost avoidance as a result of identifying and correcting an anomaly. See Playbook for more details.
Total cost of the unpredicted & correctable amount of spend * Projected Amount of Days the anomaly could have occurred if the underlying issue was not addressed
Data Sources:
Related Capabilities
FinOps Practitioner
Anomaly Management 
Workload Optimization 
Cloud Sustainability