Prioritize savings
Rank opportunities by estimated monthly impact, engineering effort, service criticality, and operational risk.
CloudCost AI turns AWS cost, usage, and resource signals into prioritized optimization actions, so teams know what to fix, who owns it, what it saves, and how to reduce spend without guessing.
Rightsize underused EC2 fleet$2,180/mo · medium effort
Apply lifecycle policy to stale S3 data$940/mo · low risk
Review NAT Gateway data transfer path$720/mo · architecture review
CloudCost AI helps engineering, finance, and leadership move from raw AWS bills to an organized savings workflow with context, ownership, and measurable results.
Rank opportunities by estimated monthly impact, engineering effort, service criticality, and operational risk.
Translate billing anomalies and technical waste into plain-language reasons, recommended fixes, and decision notes.
Follow recommendations from discovery to approval, remediation, verification, and monthly savings reporting.
CloudCost AI combines AWS spend analysis, resource intelligence, anomaly explanation, and remediation tracking in one focused FinOps workflow.
Analyze spend across services, accounts, regions, tags, resource types, and time windows.
Identify oversized, underused, idle, and forgotten compute, database, storage, and load balancing resources.
Find unattached EBS volumes, stale snapshots, high-cost storage classes, and lifecycle opportunities.
Highlight NAT Gateway, cross-AZ, cross-region, CDN, and architecture-driven transfer costs.
Surface Savings Plans and Reserved Instance opportunities with utilization, risk, and coverage context.
Generate leadership summaries, owner-based action lists, and monthly savings verification reports.
CloudCost AI is designed to make optimization work clear enough for finance and detailed enough for engineers.
Review billing, usage, resource, and trend signals.
Detect waste, anomalies, idle resources, and pricing gaps.
Rank actions by savings, risk, effort, and owner context.
Separate safe wins from changes needing engineering review.
Track implementation status and realized monthly savings.
CloudCost AI avoids one-size-fits-all recommendations. Each optimization is explained with business context, technical reasoning, risk level, approval guidance, and rollback-aware remediation steps.
Separate quick wins from changes that need performance, backup, or architecture review.
Highlight missing ownership data so teams can assign work before savings opportunities get lost.
Summarize projected and realized savings in language leadership can use for planning.
Volume has been unattached for 47 days and shows no recent write activity.
aws-cost-report-final-v4.csv
monthly-savings-tracker.xlsx
engineering-cost-notes.docx
Cloud teams do not need another dashboard full of charts. They need a clear queue of actions that explains why spend is high, what can be changed safely, and how much impact was actually delivered.
See how CloudCost AI can identify waste, prioritize safe optimization, and give your team a clearer path to lower AWS costs.