GCP Resource Optimizer

Maximize value from GCP resources and credits through strategic allocation.

Credit Burn Strategy

Credit Expiration Planning

When managing expiring credits:

• Audit current usage: gcloud billing accounts describe ACCOUNT_ID

• Calculate burn rate: Total credits ÷ Days remaining = Required daily spend

• Identify high-value uses: What creates lasting value vs. ephemeral compute?

High-Value Credit Uses

Lasting value (prioritize):

• Training ML models (artifacts persist)

• Building container images

• Generating datasets

• Running batch processing on accumulated work

Ephemeral (use strategically):

• Compute instances (gone when shut down)

• Development environments

• Testing infrastructure

Cost Optimization Patterns

Compute Engine

Right-sizing instances:

Check recommendations

gcloud recommender recommendations list
--project=PROJECT_ID
--location=ZONE
--recommender=google.compute.instance.MachineTypeRecommender

Cost-effective machine types:

Need Recommended Why

General workload e2-medium Best price/performance

Memory-intensive n2-highmem Better RAM ratio

CPU burst e2-micro/small Burstable, cheap

ML training n1 + GPU Required for accelerators

Spot-tolerant Spot VMs 60-91% discount

Preemptible/Spot VMs:

• 60-91% cheaper than standard

• Can be terminated with 30s notice

• Good for: batch jobs, fault-tolerant workloads, development

• Bad for: production, stateful services

Cloud Run

Optimizing Cloud Run:

Minimize cold starts and costs

spec: template: spec: containerConcurrency: 80 # Maximize requests per instance timeoutSeconds: 300 metadata: annotations: autoscaling.knative.dev/minScale: '0' # Scale to zero autoscaling.knative.dev/maxScale: '10' # Cap costs run.googleapis.com/cpu-throttling: 'true' # CPU only when processing

Cloud Storage

Storage class optimization:

Class Use Case Cost/GB/mo

Standard Frequent access ~$0.020

Nearline Monthly access ~$0.010

Coldline Quarterly access ~$0.004

Archive Yearly access ~$0.0012

Lifecycle rules:

{ "lifecycle": { "rule": [ { "action": {"type": "SetStorageClass", "storageClass": "NEARLINE"}, "condition": {"age": 30} }, { "action": {"type": "SetStorageClass", "storageClass": "COLDLINE"}, "condition": {"age": 90} }, { "action": {"type": "Delete"}, "condition": {"age": 365} } ] } }

BigQuery

Cost control:

-- Set maximum bytes billed #standardSQL -- @maximumBytesBilled 10000000000 SELECT * FROM dataset.table

Partitioning for cost reduction:

CREATE TABLE dataset.table PARTITION BY DATE(timestamp_column) CLUSTER BY user_id AS SELECT * FROM source_table

Budget Alerts

Set up budget alerts:

gcloud billing budgets create
--billing-account=BILLING_ACCOUNT_ID
--display-name="Monthly Budget"
--budget-amount=100USD
--threshold-rule=percent=50
--threshold-rule=percent=90
--threshold-rule=percent=100

Resource Cleanup

Find Unused Resources

Unused disks

gcloud compute disks list --filter="NOT users:*"

Unused IPs

gcloud compute addresses list --filter="status=RESERVED"

Idle VMs (by CPU)

gcloud monitoring time-series list
--filter='metric.type="compute.googleapis.com/instance/cpu/utilization"'
--interval="start=2024-01-01T00:00:00Z"

Cleanup Script

#!/bin/bash

cleanup_unused.sh - Review before running!

List (don't delete) unused resources

echo "=== Unused Disks ===" gcloud compute disks list --filter="NOT users:*" --format="table(name,zone,sizeGb)"

echo "=== Reserved IPs ===" gcloud compute addresses list --filter="status=RESERVED" --format="table(name,region,address)"

echo "=== Snapshots older than 30 days ===" gcloud compute snapshots list --filter="creationTimestamp<$(date -d '30 days ago' -Iseconds)" --format="table(name,diskSizeGb,creationTimestamp)"

Architecture Patterns for Cost

Serverless-First

Request → Cloud Run → Firestore → Done (scales to zero) (pay per op)

vs.

Request → GKE → Cloud SQL → Done (always running) (always running)

Batch Processing

Pub/Sub → Cloud Functions → BigQuery (batch load) (cheaper than streaming)

Development vs Production

Dev environment:

• Spot/preemptible VMs

• Smaller machine types

• Scale-to-zero services

• Shared resources

Prod environment:

• Committed use discounts (1-3 year)

• Right-sized dedicated instances

• Redundancy only where needed

Monitoring Setup

Enable billing export to BigQuery

gcloud beta billing accounts describe ACCOUNT_ID

Query costs

#standardSQL SELECT service.description, SUM(cost) as total_cost FROM project.dataset.gcp_billing_export_v1_* WHERE _PARTITIONTIME >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 30 DAY) GROUP BY 1 ORDER BY 2 DESC

References

• references/pricing-cheatsheet.md

• Quick pricing reference

• references/cost-queries.md

• BigQuery cost analysis queries