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AI Waste Finder

Find the idle, oversized and forgotten, with proof.

AI Waste Finder reviews usage for every resource across your estate and ranks what can be removed, scheduled or resized. Each finding shows the evidence, the risk and the monthly saving, so engineers can decide quickly and with confidence.

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AI Waste Finder5 findings · $13,440 / month
ResourceFinding and evidenceRiskSaving / mo
dev-eks-nodes · 12 nodesIdle nights and weekendsCPU 0.4% from 19:00 to 08:00, 30 daysLow$4,080
orders-db · db.r6g.4xlargeOversizedAvg CPU 9%, p95 21%, 14 daysMedium$2,520
412 EBS volumes and snapshotsUnattached or orphanedNo attachment for 60+ daysLow$1,560
api-fleet · m6i.2xlarge x 18On-demand running 24x7720 steady hours per monthLow$4,320
us-east-1 to eu-west-1Cross-region transfer38 TB per month between regionsReview$960
Read-only. Your engineers decide and make every change.

What it does

Built for the engineers who make the change, and the finance team that checks it.

Idle schedules

Dev, test and staging environments that sit unused at night and on weekends, with a suggested on and off schedule.

Oversized compute

VMs, node groups and databases where CPU, memory and network stay far below what is provisioned.

Orphaned storage

Unattached disks, old snapshots, empty buckets and backups past their retention, listed with their age.

Network waste

Idle load balancers, unused public IPs, NAT gateway hot spots and traffic crossing regions without a reason.

Storage tiers

Objects that have not been read in 90 days and would cost less in an infrequent access or archive tier.

Ranked by value and risk

Findings ordered by saving and confidence, so the first ten minutes of review deliver most of the value.

How it works

AI Waste Finder, step by step.

Responsible AI

  • Access is read-only. We never change, stop or delete resources
  • Every suggestion shows its evidence and risk level
  • Savings estimates are estimates. Realised savings come from actual bills
  • Billing and usage data is encrypted and never sold
  1. 01

    Usage is collected

    Metrics from CloudWatch, Azure Monitor and Cloud Monitoring are read for 30 to 90 days, alongside billing data.

  2. 02

    Patterns are learned

    Models learn daily and weekly patterns for each resource, so a batch job that runs on Sundays is not mistaken for idle.

  3. 03

    Findings are scored

    Each finding gets a saving estimate, a confidence level and a risk level based on how the resource is used.

  4. 04

    Owners decide

    Owners accept, snooze or dismiss. Dismissed findings teach the model what your team considers normal.

Questions

About AI Waste Finder

Can the AI turn things off?

No. The connection is read-only and cannot change resources. Your engineers make every change with their own tools and approvals.

How do you avoid false positives?

Findings need a long enough usage window, they consider weekly and monthly cycles, and each one shows the data behind it. Anything uncertain is marked for review instead of presented as a saving.

Previous Spend MapNextRightsizing Lab
Your forecast

Find your savings free.

Each month we spendonand we want next month to be clearer.

Read-only connection · First report usually within a day · No card needed for the report