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Cloud Cost Optimization Agent

Information TechnologyCloud Cost Management

Analyzes cloud billing and usage data continuously to identify idle resources, oversized instances, and unused reservations, then recommends or executes cost-saving actions.

4
Process steps
6
Integrations
3
Data inputs

Cloud spend routinely grows faster than usage because idle instances, orphaned storage volumes, and oversized compute sit unnoticed between monthly finance reviews

Engineering teams provision resources for peak load and rarely revisit sizing once traffic patterns change, and reserved instance or savings plan commitments often go underutilized without anyone tracking utilization against actual consumption

Manual cost audits are time-consuming, happen infrequently, and by the time a FinOps team spots waste it has already accumulated for weeks

This agent continuously scans billing and utilization data across cloud accounts, identifies specific waste with dollar-value impact, and either recommends changes for approval or automatically executes low-risk actions like deleting unattached volumes

The agent pulls billing exports and resource utilization metrics from cloud provider APIs, applies rulesets and anomaly detection to spot idle, oversized, or orphaned resources, and estimates the dollar savings of each finding. Findings are ranked by savings potential and risk, routed for approval where changes are non-trivial, and automatically remediated where policy allows (e.g., deleting resources idle for 30+ days). A recurring report tracks cumulative savings and spend trends by team and service.

1

Collect Billing and Usage Data

  • Pull detailed billing exports from cloud provider cost APIs
  • Gather CPU, memory, and network utilization metrics per resource
  • Tag spend by team, project, and environment
  • Track reserved instance and savings plan coverage
Outcome: A complete, tagged dataset of cloud spend and utilization across all connected accounts.
2

Identify Waste and Oversizing

  • Flag instances with sustained low CPU/memory utilization
  • Detect orphaned volumes, unattached IPs, and idle load balancers
  • Identify underutilized reserved capacity
  • Estimate dollar savings per finding
Outcome: A ranked list of specific, dollar-quantified savings opportunities across the environment.
3

Recommend or Execute Remediation

  • Route rightsizing recommendations to resource owners for approval
  • Auto-remediate low-risk waste per configured policy
  • Suggest reserved instance or savings plan adjustments
  • Log every action taken with before/after cost impact
Outcome: Waste is eliminated quickly with an auditable trail of every cost action taken.
4

Report Savings and Trends

  • Generate weekly cost trend reports by team and service
  • Track cumulative realized savings against identified opportunities
  • Alert on anomalous spend spikes in near real time
  • Forecast next-month spend based on current trajectory
Outcome: Finance and engineering leaders get continuous visibility into cloud spend and savings achieved.
AWS Cost Explorer
Pulls detailed billing and usage data
Azure Cost Management
Ingests spend and resource metrics
Google Cloud Billing API
Retrieves usage and cost exports
Slack
Sends savings recommendations and approval requests
Terraform
Executes approved rightsizing changes as infrastructure code
ServiceNow
Logs remediation actions for audit and change tracking