gcp-cost-governance

name	gcp-cost-governance
description	Playbook for setting up GCP billing visibility and cost controls — billing export to BigQuery, budget alerts, label-based attribution, committed-use discounts, and the common cost leaks to eliminate first.

GCP Cost Governance

When to invoke

Use when standing up cost visibility for a new GCP project or organization, investigating a billing surprise, or building the FinOps foundation before spend scales. Pairs with gcp-resource-hierarchy (folder/project structure is the prerequisite for meaningful attribution).

Step 1 — Billing export to BigQuery (non-negotiable first step)

Enable detailed billing export — this is the raw material for every cost analysis:

resource "google_billing_account_iam_member" "bq_export" {
  billing_account_id = var.billing_account_id
  role               = "roles/billing.admin"
  member             = "serviceAccount:${google_service_account.billing_export.email}"
}

In the GCP console: Billing → Billing export → BigQuery export → Edit settings:

• Enable Standard usage cost export (required).
• Enable Detailed usage cost export (adds resource-level granularity; recommended).
• Enable Pricing export (needed for unit economics).

Dataset location should match your primary region. Export begins from the date it is enabled — there is no backfill.

Step 2 — Label strategy

Labels are the attribution axis. Enforce them via org policy:

resource "google_org_policy_policy" "require_labels" {
  name   = "organizations/${var.org_id}/policies/gcp.resourceLocations"
  parent = "organizations/${var.org_id}"
  # Use Terraform google_project module with mandatory label defaults
}

Minimum mandatory labels on all resources:

Label key	Example values	Purpose
env	prod, staging, dev	Environment split
team	platform, data, commerce	Team attribution
service	api-gateway, ml-training	Service-level cost
cost-center	eng-123	Finance allocation

Verify label coverage:

-- BigQuery billing export
SELECT service.description, labels, sum(cost) AS total
FROM `billing_dataset.gcp_billing_export_*`
WHERE DATE(_PARTITIONTIME) >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
  AND (labels IS NULL OR array_length(labels) = 0)
GROUP BY 1, 2
ORDER BY 3 DESC
LIMIT 20;

Any row with null/empty labels is unattributed spend.

Step 3 — Budget alerts

Create budgets at the project level (and optionally at the folder level):

resource "google_billing_budget" "per_project" {
  billing_account = var.billing_account_id
  display_name    = "${var.project_id}-monthly-budget"

  budget_filter {
    projects = ["projects/${var.project_number}"]
  }

  amount {
    specified_amount {
      currency_code = "USD"
      units         = "500"
    }
  }

  threshold_rules { threshold_percent = 0.5 }
  threshold_rules { threshold_percent = 0.9 }
  threshold_rules { threshold_percent = 1.0 }
  threshold_rules { threshold_percent = 1.25 }  # overage alert

  all_updates_rule {
    pubsub_topic = google_pubsub_topic.billing_alerts.id
  }
}

Route the Pub/Sub topic to Slack/PagerDuty via a Cloud Function or Eventarc.

Step 4 — Committed-use discounts (CUDs)

Workload	CUD type	Savings
Steady-state GCE VMs	Resource-based CUD (1 or 3 year)	Up to 57%
Cloud Run / GKE Autopilot	Spend-based CUD	Up to 17%
BigQuery (on-demand → slots)	BigQuery editions + reservations	Up to 40% for predictable query load

Baseline before committing: run Recommender (GCP console → Committed Use Discounts) for a 30-day usage analysis recommendation.

Do not commit before you have 30+ days of stable usage — a CUD on a workload you are about to resize is waste.

Step 5 — Top cost leaks to check first

Run this BigQuery query weekly:

SELECT
  service.description AS service,
  sku.description AS sku,
  SUM(cost) AS cost_usd,
  SUM(usage.amount) AS usage_amount,
  usage.unit
FROM `billing_dataset.gcp_billing_export_resource_*`
WHERE DATE(_PARTITIONTIME) >= DATE_SUB(CURRENT_DATE(), INTERVAL 7 DAY)
GROUP BY 1, 2, 5
ORDER BY 3 DESC
LIMIT 50;

Common culprit	Diagnostic	Fix
Idle GCE VMs	status = RUNNING, low CPU	Stop or delete; use preemptible/spot for batch
Oversized Cloud SQL	database_id, high memory cost	Downsize tier; use read replicas only where needed
Egress to internet	Networking → external egress SKUs	Use Private Google Access; keep traffic in-region
Unused persistent disks	Disk SKU with no attached VM	Delete orphaned disks
BigQuery on-demand scans	High Bytes processed per query	Partition + cluster tables; use query cost estimator
Artifact Registry storage	Old image layers accumulating	Add lifecycle policy to delete untagged images

Step 6 — Cost anomaly detection

Enable GCP Recommender / Cost Insights and the Billing anomaly detection feature (under Billing → Cost Management). It sends alerts when spend increases >10% week-over-week on a service.

Supplement with a BigQuery scheduled query that fires when a service's 7-day spend exceeds 2× its 30-day average:

-- anomaly_detect.sql (run daily via scheduled query)
WITH baseline AS (
  SELECT service.description, AVG(daily_cost) AS avg_cost
  FROM (
    SELECT DATE(usage_start_time) AS d, service.description, SUM(cost) AS daily_cost
    FROM `billing_dataset.gcp_billing_export_*`
    WHERE DATE(usage_start_time) >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
    GROUP BY 1, 2
  )
  GROUP BY 1
),
recent AS (
  SELECT service.description, SUM(cost) AS week_cost
  FROM `billing_dataset.gcp_billing_export_*`
  WHERE DATE(usage_start_time) >= DATE_SUB(CURRENT_DATE(), INTERVAL 7 DAY)
  GROUP BY 1
)
SELECT r.description, r.week_cost, b.avg_cost * 7 AS expected_7d_cost
FROM recent r JOIN baseline b USING (description)
WHERE r.week_cost  b.avg_cost    
  r.week_cost ;

Pitfalls

• Not enabling billing export on day one — there is no backfill; you lose the historical baseline needed for anomaly detection and CUD decisions.
• Sharing a billing account across unrelated orgs or customers — attribution becomes impossible without a separate billing account per top-level org.
• Setting a budget alert at 100% only — by the time it fires, the month's budget is gone. Add 50% and 90% thresholds.
• Buying CUDs for dev/test workloads — committed use discounts apply across the org, but committing against unpredictable workloads is waste; measure first.