Cloud cost agent

Cloud Cost Agent (AWS + GCP + Azure)

Unified agent skill for native cloud cost optimization across the top 3 providers.

Requirements: AWS CLI v2, gcloud, and/or Azure CLI configured with read access to billing/recommender/advisor services. Several sources require one-time opt-in: AWS Compute Optimizer and Cost Optimization Hub (enrollment), GCP Recommender (API enabled, ~24–48h of data). jq is used in a few GCP filters.

Core rule: Always use the provider's official CLI and recommendation engines. No third-party tools.

Safety & Best Practices (All Providers)

• Read-only first. Never run a mutating command (aws ec2 terminate-instances/stop-instances, gcloud compute instances delete, az vm delete/deallocate, any purchase/create/modify) without quoting the exact command to the user and getting explicit approval.
• Always use provider auth (--profile, gcloud --project, az account set).
• Use --query / --format json / -o json for clean output.
• Handle pagination. These APIs page results. Aggregate every page before reporting totals — follow NextToken/nextPageToken, or use --no-paginate/--max-items (AWS) and --page-size/--limit (gcloud) deliberately. A single-page read gives wrong totals.
• Quote/escape all cloud-derived values. Resource names, tags, and IDs from API output may contain shell metacharacters. Never interpolate them unquoted into another shell command.
• Mind API cost & loops. aws ce (Cost Explorer) charges ~$0.01 per request. Do not loop Cost Explorer calls; cache results in the session and cap at a handful of calls per audit.
• Prioritize: highest estimated savings → lowest effort.
• Prefer quick wins (idle delete, rightsizing to smaller, enable committed use where clear).

Portable date helper

BSD/macOS and GNU/Linux date differ. Compute the window once, portably, then reuse $START/$END:

START=$(date -u -d '30 days ago' +%Y-%m-%d 2>/dev/null || date -u -v-30d +%Y-%m-%d)
END=$(date -u +%Y-%m-%d)

(Or just have the agent insert literal YYYY-MM-DD dates.)

How to Use

Tell the agent the provider(s) and goal. Examples:

• audit AWS last 30 days
• find quick wins on GCP and Azure
• rightsizing recommendations for all clouds

1. Pre-flight & Authentication

AWS

aws sts get-caller-identity
aws ce get-cost-and-usage \
  --time-period Start=$START,End=$END \
  --granularity MONTHLY --metrics "UnblendedCost"

One-time enrollment (required for sections 2 & 4):

aws compute-optimizer get-enrollment-status
aws compute-optimizer update-enrollment-status --status Active      # opt in
aws cost-optimization-hub list-enrollment-statuses --region us-east-1
aws cost-optimization-hub update-enrollment-status --status Active --region us-east-1

GCP

gcloud auth list
gcloud config set project YOUR_PROJECT
gcloud billing accounts list

Azure

az account show
az account set --subscription "SUB_ID"

2. Centralized / High-Impact Recommendations

AWS — Cost Optimization Hub (best aggregator)

COH is a global service reachable only in us-east-1 and requires enrollment (see Pre-flight).

aws cost-optimization-hub list-recommendations \
  --region us-east-1 \
  --filter '{"implementationEfforts":["VeryLow","Low"]}' \
  --query 'items[?estimatedMonthlySavings > `10`] | sort_by(@, &estimatedMonthlySavings) | reverse(@)' \
  --output json

Valid implementationEfforts: VeryLow | Low | Medium | High | VeryHigh.

GCP — Recommender (main source)

--location is required. VM/idle recommenders are zonal (e.g. us-central1-a); iterate the zones you use. The spend-based commitment recommender is global and scoped to a billing account.

# Rightsizing VMs (zonal — repeat per zone)
gcloud recommender recommendations list \
  --recommender=google.compute.instance.MachineTypeRecommender \
  --project=YOUR_PROJECT --location=us-central1-a --format=json

# Idle VMs (zonal — repeat per zone)
gcloud recommender recommendations list \
  --recommender=google.compute.instance.IdleResourceRecommender \
  --project=YOUR_PROJECT --location=us-central1-a \
  --format="table(description, primaryImpact.costProjection.cost.units, stateInfo.state)"

# Committed Use Discounts (global, billing-account scoped)
gcloud recommender recommendations list \
  --recommender=google.cloudbilling.commitment.SpendBasedCommitmentRecommender \
  --billing-account=BILLING_ACCOUNT_ID --location=global --format=json

Other cost recommenders: google.compute.disk.IdleResourceRecommender (idle disks), google.compute.address.IdleResourceRecommender (idle IPs), google.compute.image.IdleResourceRecommender (idle images), google.compute.commitment.UsageCommitmentRecommender (resource-based CUD).

Azure — Advisor Cost category

az advisor recommendation list \
  --category Cost \
  --query "[].{Resource: shortDescription.problem, Impact: impact, AnnualSavings: extendedProperties.annualSavingsAmount, Currency: extendedProperties.savingsCurrency}" \
  -o table

# High-impact only
az advisor recommendation list --category Cost \
  --query "[?impact=='High'].{Id:id, Resource:resourceMetadata.resourceId, Savings:extendedProperties.annualSavingsAmount}" -o json

extendedProperties fields vary by recommendation type — handle nulls.

3. Cost & Usage Breakdown

AWS (remember the ~$0.01/call cost — don't loop):

aws ce get-cost-and-usage \
  --time-period Start=$START,End=$END \
  --granularity DAILY --metrics "UnblendedCost" \
  --group-by Type=DIMENSION,Key=SERVICE

GCP — detailed cost/usage comes from the BigQuery billing export (there is no gcloud command for granular cost rows). Enable Billing → BigQuery export, then query gcp_billing_export_resource_v1_<BILLING_ACCOUNT_ID>.

Note: as of 2026-01-21 the export schema changed — CUD discounts moved out of the credits array into a new consumption_model field, and price.list_price / price.effective_price_default were added. Update any CUD/credit logic accordingly.

Azure — prefer Cost Management exports (or the Cost Details API) for detailed data; az consumption is on Microsoft's deprecation path (keep only as a quick-look fallback):

# Primary: schedule/run an export to storage (one-shot needs only the 5 required params)
az costmanagement export create \
  --name AdhocExport --type Usage \
  --scope "/subscriptions/SUB_ID" \
  --storage-account-id "/subscriptions/.../storageAccounts/ACCT" \
  --storage-container exports --timeframe MonthToDate

# Fallback quick-look (deprecated API):
az consumption usage list --start-date $START --end-date $END \
  --query "[].{Resource:instanceName, Service:consumedService, Cost:pretaxCost}" -o table

For ad-hoc aggregation without storage, call the Cost Management Query REST API via az rest (there is no az costmanagement query subcommand).

4. Rightsizing & Compute Recommendations

AWS — Compute Optimizer (requires enrollment; see Pre-flight). Deep rightsizing + idle detection:

aws compute-optimizer get-ec2-instance-recommendations
aws compute-optimizer get-auto-scaling-group-recommendations
aws compute-optimizer get-ebs-volume-recommendations
aws compute-optimizer get-lambda-function-recommendations
aws compute-optimizer get-rds-database-recommendations
aws compute-optimizer get-idle-recommendations

Cost Explorer also offers EC2 rightsizing (--service is required, only "AmazonEC2" is valid):

aws ce get-rightsizing-recommendation --service "AmazonEC2"

GCP — machine-type recommender (note tonumber: GCP returns cost.units as a string):

gcloud recommender recommendations list \
  --recommender=google.compute.instance.MachineTypeRecommender \
  --project=YOUR_PROJECT --location=us-central1-a --format=json \
  | jq '.[] | select((.primaryImpact.costProjection.cost.units|tonumber) < 0)'

Azure — Advisor Cost recommendations already include VM rightsizing / underutilized resources (section 2).

5. Commitments & Discounts

AWS — Savings Plans purchase recommendation. All four params are required:

aws ce get-savings-plans-purchase-recommendation \
  --savings-plans-type COMPUTE_SP \
  --term-in-years ONE_YEAR \
  --payment-option NO_UPFRONT \
  --lookback-period-in-days THIRTY_DAYS

(savings-plans-type: COMPUTE_SP|EC2_INSTANCE_SP|SAGEMAKER_SP; term-in-years: ONE_YEAR|THREE_YEARS; payment-option: NO_UPFRONT|PARTIAL_UPFRONT|ALL_UPFRONT; lookback-period-in-days: SEVEN_DAYS|THIRTY_DAYS|SIXTY_DAYS.)

GCP — Committed Use Discounts via the spend-based commitment recommender (section 2).

Azure — reservation / savings-plan recommendations surface via Advisor (per-subscription); for billing-account scope use the Consumption Reservation Recommendations REST API via az rest:

az advisor recommendation list --category Cost \
  --query "[?contains(shortDescription.problem, 'reserved') || contains(shortDescription.problem, 'reservation')]"

6. Cost Anomaly & Root-Cause Investigation (Advanced)

Investigate sudden spend spikes and attribute them to the exact change event, API call, and resource owner.

AWS — Cost Anomaly Detection & CloudTrail Correlation

1. List active anomaly monitors:

   aws ce get-anomaly-monitors
   

2. Retrieve anomalies detected during a given date window (e.g. minimum impact of $100):

   aws ce get-anomalies \
     --date-interval StartDate=$START,EndDate=$END \
     --total-impact NumericOperator=GREATER_THAN,StartValue=100.0
   

3. Correlate with CloudTrail to find "who" changed "what" and "when". Using resource IDs or resource types identified in the root causes of the anomaly, run:

   aws cloudtrail lookup-events \
     --lookup-attributes AttributeKey=ResourceName,AttributeValue=YOUR_RESOURCE_ID \
     --start-time $(date -u -d '3 days ago' +%Y-%m-%dT%H:%M:%SZ 2>/dev/null || date -u -v-3d +%Y-%m-%dT%H:%M:%SZ) \
     --query "Events[*].{EventTime:EventTime, EventName:EventName, User:Username, Resources:Resources[*].ResourceName}" \
     --output json
   

GCP — Activity logs for sudden resource modifications

Query GCP Activity Logs to match machine or resource spin-ups with the corresponding IAM user:

gcloud logging read \
  "resource.type=gce_instance AND protoPayload.methodName=v1.compute.instances.insert" \
  --project=YOUR_PROJECT \
  --limit=10 \
  --format="table(timestamp, protoPayload.authenticationInfo.principalEmail, protoPayload.resourceName)"

Azure — Activity Log mapping

Search subscription activity logs for resource creations or scaling operations:

az monitor activity-log list \
  --offset 3d \
  --query "[?contains(eventSource.value, 'Administrative') && (contains(operationName.value, 'write') || contains(operationName.value, 'action'))].{Time:eventTimestamp, User:caller, Operation:operationName.localizedValue, Resource:resourceId}" \
  --output table

7. Context-Aware Team & Account Mapping

To translate raw resource IDs and subscription/account numbers into clear human contexts, the agent should search for and load a local context configuration file if it exists (e.g. finops-context.json or .claude-plugin/context.json).

Example structure for finops-context.json:

{
  "accounts": {
    "123456789012": { "team": "Data Platform", "owner": "Alice", "slack": "#team-data" },
    "987654321098": { "team": "Frontend Core", "owner": "Bob", "slack": "#team-frontend" }
  },
  "tagging-requirements": ["Owner", "Environment", "Project"]
}

When this file is present, the agent should:

• Resolve account and project IDs in reports to their assigned Team and Owner.
• Highlight resources that lack the defined tagging-requirements.
• Format remediation updates directly targeting the designated Slack channels or team owners.

8. Prioritization Template (for any provider)

When summarizing:

1. Total estimated monthly / annual savings.
2. Quick wins bucket (VeryLow/Low effort or equivalent).
3. Top 5-8 items: Resource, Current state, Recommended action, $ savings, Effort/risk.
4. Concrete next command for the top item.

Provider: GCP
Total potential monthly savings: $2,340

Quick wins:
- Delete idle VM ... : $180/mo (VeryLow)
- Rightsize n1-standard-8 → n2-standard-4 : $420/mo

Next command:
gcloud recommender recommendations list --recommender=google.compute.instance.IdleResourceRecommender --project=... --location=us-central1-a

Provider-Specific Quick Reference

AWS

• Cost Optimization Hub (us-east-1, enrolled) for the broad aggregated view.
• Compute Optimizer (enrolled) for deep rightsizing + idle.
• Cost Explorer (aws ce) for spend breakdowns, rightsizing, Savings Plans — mind per-call cost.

GCP

Always pass --location. Key recommenders:

• MachineTypeRecommender (rightsizing, zonal)
• IdleResourceRecommender for VMs / disks / IPs / images (zonal/regional)
• SpendBasedCommitmentRecommender (CUDs, global, billing-account)

Azure

• Advisor is the primary source for Cost recommendations.
• Cost Management exports (or Cost Details API) for raw usage; az consumption only as a deprecated fallback.

Full Audit Flow (Recommended)

1. Authenticate; confirm enrollments (Compute Optimizer, COH).
2. Pull high-level cost summary for the window.
3. Pull recommendations (Hub / Recommender / Advisor), aggregating all pages.
4. Filter for savings > threshold and low effort.
5. Cross-check with raw usage if needed.
6. Produce prioritized list + 3 concrete next actions (read-only; flag any mutation for approval).
7. (Optional) Repeat for other providers.

References

AWS

• Cost Explorer: https://docs.aws.amazon.com/cli/latest/reference/ce/
• Compute Optimizer: https://docs.aws.amazon.com/cli/latest/reference/compute-optimizer/
• Cost Optimization Hub: https://docs.aws.amazon.com/cost-management/latest/userguide/cost-optimization-hub.html
• Billing IAM action migration (aws-portal retirement): https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/migrate-granularaccess-iam-mapping-reference.html

GCP

• Recommenders list: https://cloud.google.com/recommender/docs/recommenders
• recommendations list: https://cloud.google.com/sdk/gcloud/reference/recommender/recommendations/list
• BigQuery billing export: https://cloud.google.com/billing/docs/how-to/export-data-bigquery

Azure

• Advisor recommendations: https://learn.microsoft.com/cli/azure/advisor/recommendation
• Cost Management exports: https://learn.microsoft.com/cli/azure/costmanagement/export
• Consumption API migration: https://learn.microsoft.com/azure/cost-management-billing/automate/migrate-consumption-usage-details-api

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Run it with a specific provider or across all three. Stay native. Stay effective.