Cloud Spend Review

Purpose

Find and remove cloud waste with evidence, and put guardrails in place so it does not return. This is read-and-recommend work: rightsizing over-provisioned compute against real utilization, deleting idle and orphaned resources (unattached disks, idle load balancers, stale snapshots, unused IPs), moving cold data to cheaper storage tiers, attacking data-egress charges, applying reserved/spot/committed-use discounts to steady baseload, enforcing cost-allocation tagging, and wiring cost-anomaly alerts. The output is a ranked savings list with the evidence behind each line and the safe action to capture it — never a blind "delete to save money."

When to use

• The monthly cloud bill is rising and the largest line items have not been attributed to a service or team.
• Instances/databases were sized for peak or "to be safe" and run far below their provisioned CPU/memory.
• You suspect orphaned resources (disks with no instance, unattached IPs, old snapshots, idle load balancers).
• A surprising data-transfer/egress charge appears and the cross-region or internet path is unclear.
• Steady baseload runs entirely on on-demand pricing with no reserved/committed/spot coverage.
• Untagged spend makes it impossible to attribute cost to teams or environments.

When not to use

• The workload is already efficiently sized and discount-covered, and the remaining spend is genuine working cost — there is no waste to cut.
• The "cost" is per-token LLM/API billing — use LLM API cost optimization instead.
• A one-time spike came from a known, finished batch job that has already stopped.

Procedure

1. Attribute the bill before cutting anything. Pull cost grouped by service, region, account/project, and tag for the last 30–90 days. Rank the top line items; a few services usually dominate (the 80/20 rule holds for cloud bills). You cannot prioritize savings you have not attributed, and cutting the small lines first wastes effort.
2. Rightsize against real utilization. For compute and databases, compare provisioned vCPU/memory against actual p95 utilization over weeks. Persistently low utilization (e.g., p95 CPU under ~20–40%) is a rightsizing candidate — step down a size or move to autoscaling, not delete. Account for memory as well as CPU; a memory-bound workload may need its current size even at low CPU.
3. Hunt idle and orphaned resources. List resources with no attachment or no traffic: unattached block volumes, unassociated static IPs, idle/empty load balancers, stopped instances still billing for disks, and snapshots/AMIs older than the retention policy. These accrue silently because nobody owns a resource that nothing points at.
4. Tier storage by access pattern. Identify cold objects (not read in N days) and move them to infrequent-access/archive tiers via lifecycle rules. Check that frequently-read data is NOT in an archive tier where retrieval fees and latency would dominate — a wrong tier can cost more than the standard tier it replaced.
5. Attack egress. Find cross-region, cross-AZ, and internet egress charges. Co-locate chatty services, put a CDN in front of repeated public downloads, and use private/VPC endpoints to keep traffic off the metered internet path. Egress is metered per GB and rarely appears as a single obvious line, so it hides in plain sight.
6. Cover baseload with commitments and spot. Quantify the steady 24/7 floor and cover it with reserved instances / committed-use / savings plans; run fault-tolerant, interruptible work (batch, CI, stateless workers) on spot/preemptible. Do not over-commit beyond the proven floor, because an unused commitment is locked-in waste, not a discount.
7. Schedule non-production environments. Dev, staging, and test environments rarely need to run 24/7. Confirm they auto-stop outside business hours; an always-on non-prod fleet is one of the most common and easiest-to-fix sources of waste.
8. Enforce tagging and wire anomaly alerts. Require cost-allocation tags (team, env, service) and report untagged spend. Turn on the provider's cost-anomaly detection plus a budget alert so the next regression pages someone instead of surfacing in next month's invoice — guardrails stop waste from recurring after this review ends.

Concrete checks

• ] Spend is attributed by service, region, account/project, and tag; the top line items are ranked.
• ] Compute/database provisioned size is compared against weeks of real p95 utilization (not a single reading).
• ] Unattached volumes, unassociated IPs, idle load balancers, and stale snapshots/images are enumerated.
• ] Stopped instances are checked for disks/IPs that still bill while stopped.
• ] Storage lifecycle rules move cold data to cheaper tiers; hot data is not stranded in archive.
• ] Cross-region/cross-AZ and internet egress charges are identified and attributed to a path.
• ] Steady baseload coverage by reserved/committed/savings plans is quantified vs. on-demand.
• ] Interruptible workloads run on spot/preemptible where safe.
• ] A mandatory tagging policy exists and untagged spend is measured.
• ] Non-production environments are scheduled to stop outside working hours, not left always-on.
• ] Log and metrics retention windows match what is actually queried, not an open-ended default.
• ] Cost-anomaly detection and a budget alert are enabled.

Commands or Templates

bash
# --- AWS: spend grouped by service (last 30 days), top drivers first ---
aws ce get-cost-and-usage \
  --time-period Start=$(date -v-30d +%F),End=$(date +%F) \
  --granularity MONTHLY --metrics "UnblendedCost" \
  --group-by Type=DIMENSION,Key=SERVICE \
  --query 'ResultsByTime[].Groups[].{svc:Keys[0],usd:Metrics.UnblendedCost.Amount}'

# Orphaned EBS volumes (status 'available' = attached to nothing)
aws ec2 describe-volumes --filters Name=status,Values=available \
  --query 'Volumes[].{id:VolumeId,gib:Size,az:AvailabilityZone,created:CreateTime}'

# Unassociated Elastic IPs (billed when not attached)
aws ec2 describe-addresses \
  --query 'Addresses[?AssociationId==null].{ip:PublicIp,alloc:AllocationId}'

# Snapshots older than retention (older than ~90 days here)
aws ec2 describe-snapshots --owner-ids self \
  --query "Snapshots[?StartTime<='$(date -v-90d +%F)'].{id:SnapshotId,gib:VolumeSize,when:StartTime}"

# Untagged running instances (no 'team' tag = unattributed spend)
aws ec2 describe-instances --filters Name=instance-state-name,Values=running \
  --query 'Reservations[].Instances[?!not_null(Tags[?Key==`team`])].InstanceId'

bash
# --- GCP: spend by service and recommender (rightsizing) ---
gcloud billing accounts list
# Idle/rightsizing suggestions from the recommender API
gcloud recommender recommendations list \
  --recommender=google.compute.instance.MachineTypeRecommender \
  --location=<zone> --format='table(description, primaryImpact.costProjection.cost)'
# Unattached persistent disks
gcloud compute disks list --filter="-users:*" \
  --format='table(name, sizeGb, zone, creationTimestamp)'

bash
# --- Rightsizing evidence: p95 CPU over 14 days (AWS CloudWatch) ---
aws cloudwatch get-metric-statistics \
  --namespace AWS/EC2 --metric-name CPUUtilization \
  --dimensions Name=InstanceId,Value=<id> \
  --start-time $(date -v-14d +%FT%T) --end-time $(date +%FT%T) \
  --period 3600 --extended-statistics p95 \
  --query 'Datapoints[].ExtendedStatistics.p95' | sort -rn | head

json
// S3 lifecycle rule: tier cold objects down, then expire — recommend, do not auto-apply.
{
  "Rules": [{
    "ID": "tier-and-expire-logs",
    "Filter": { "Prefix": "logs/" },
    "Status": "Enabled",
    "Transitions": [
      { "Days": 30,  "StorageClass": "STANDARD_IA" },
      { "Days": 90,  "StorageClass": "GLACIER" }
    ],
    "Expiration": { "Days": 365 }
  }]
}

bash
# Idle load balancers: ALBs with zero processed requests over the window are waste.
aws cloudwatch get-metric-statistics --namespace AWS/ApplicationELB \
  --metric-name RequestCount --statistics Sum --period 86400 \
  --dimensions Name=LoadBalancer,Value=<lb-arn-suffix> \
  --start-time $(date -v-14d +%FT%T) --end-time $(date +%FT%T) \
  --query 'sum(Datapoints[].Sum) == `0` && `IDLE` || `IN_USE`'

Common issues & anti-patterns

• Deleting before attributing. Removing resources to "save money" without confirming they are idle risks an outage; gather evidence (no attachment, no traffic, age) first.
• Rightsizing on one data point. A single low-CPU reading is meaningless; size to weeks of p95, accounting for batch/peak windows.
• Over-committing reservations. Buying reserved/committed capacity above the proven 24/7 floor locks in spend you do not use; cover only the baseload.
• Putting hot data in archive. Archive tiers have retrieval fees and latency; moving frequently-read data there can cost more than it saves.
• Ignoring egress. Cross-region/AZ and internet transfer is a top hidden cost; chatty cross-region calls and un-CDN'd downloads quietly dominate the bill.
• Forgotten stopped instances. A stopped VM still bills for its attached disks and IPs; "stopped" is not "free."
• No tags, no accountability. Untagged spend cannot be attributed, so waste has no owner and recurs.
• Always-on non-production environments. Dev/staging fleets left running 24/7 burn money outside working hours for no benefit; schedule them to stop.
• Over-provisioned logging/metrics retention. Keeping verbose logs and high-resolution metrics far longer than anyone queries them is a silent, compounding storage cost.
• Premium support/managed tiers chosen by default. Paying for the highest service tier on workloads that do not need it (e.g., multi-AZ on a throwaway environment) inflates the bill without value.
• No anomaly alert. Without anomaly detection and a budget alert, the next runaway resource is discovered on the invoice.

Required output

Produce a report containing:

1. Spend attribution — top line items by service/region/account/tag, with each line's share of the bill.
2. Rightsizing table — resource, provisioned size, p95 utilization, recommended size, and estimated monthly saving.
3. Idle/orphaned list — each unattached/idle/stale resource with evidence (no attachment, no traffic, age) and the saving.
4. Storage & egress — cold-data tiering opportunities and the egress paths driving transfer cost, each with a fix.
5. Commitment plan — quantified baseload and recommended reserved/committed/spot coverage (and what NOT to commit).
6. Governance — tagging gaps and the anomaly/budget alert status.
7. Ranked savings + next safe action — total estimated monthly saving, prioritized, with the safest first step (usually deleting evidenced orphans or tiering cold storage).

Safety

• This is a read-and-recommend review: do not delete, resize, or terminate any resource; produce evidenced recommendations and let an owner act with approval.
• A resource being idle now does not prove it is safe to remove (DR standby, seasonal, scheduled job) — flag and confirm ownership before recommending deletion.
• Never print account IDs, access keys, or credentials in the report; reference accounts/projects by name or alias and redact identifiers.
• Use read-only cost/inventory APIs; do not run write/delete CLI calls during the review.
• Verify a snapshot/AMI is not the only backup of important data before recommending its deletion.
• Treat reserved/committed purchases as a finance decision; recommend the coverage, but do not buy commitments.