Browse GCP detectors for orphaned resources, leftover storage, and BigQuery cost optimization opportunities hidden in everyday platform choices.
Use this hub to find orphaned GCP resources, leftover storage, and BigQuery cost issues before they turn into routine baseline spend.
Why this matters
These are the GCP waste patterns most likely to hide in storage policy, stale infrastructure, and data-platform settings.
Missing lifecycle rules and manual retention can turn routine object storage into long-tail spend when no one closes the cleanup loop.
Persistent disks and other stale resources often outlive the compute or feature they supported because decommissioning was partial, cautious, or unowned.
Billing-model choices and unused analytical outputs can keep compounding cost even when query activity and retention needs have shifted.
How to use this page
Use this page to move from a GCP cost symptom to the platform decision or cleanup motion behind it.
GCP category guides
These guides group related GCP detectors so teams can move from single findings to broader storage and stale-resource cleanup work.
Uncover orphaned GCP infrastructure spend through unattached persistent disks and the ownership drift that leaves old environments partially retired.
Category guide
Orphaned and Stale Resources
Reduce GCP storage costs by finding BigQuery billing model mismatches and GCS buckets whose lifecycle cleanup is still fully manual.
Category guide
Storage Cost Optimization
Published GCP detectors
These detector pages cover the concrete GCP signals that support the broader cleanup themes above.
BigQuery storage billing model mismatch creates avoidable cost when a dataset stays on the wrong billing model for its churn, compression, and retention pattern. Teams often miss it because query cost gets reviewed more often than dataset storage behavior.
Potential savings
$150 to $3,000 / month
$1,800 to $36,000 / year
Detector ID
gcp-bigquery-storage-billing-model-mismatch
GCS bucket lifecycle policy cleanup becomes necessary when old objects keep billing because no lifecycle rule ever deletes or transitions them. This usually happens in log, export, backup, and artifact buckets that are created quickly and rarely reviewed later.
Potential savings
$50 to $1,400 / month
$600 to $16,800 / year
Detector ID
gcp-gcs-bucket-missing-lifecycle
Cloud Storage buckets with versioning enabled can accumulate archived object generations indefinitely when lifecycle rules do not effectively clean up versioned object buildup. Teams often miss it because current object counts stay stable while older generations continue billing underneath.
Potential savings
$75 to $1,600 / month
$900 to $19,200 / year
Detector ID
gcp-gcs-bucket-versioning-no-noncurrent-cleanup
Compute Engine Persistent Disks continue billing after instance deletion or rebuild when they remain detached beyond a conservative review window and do not carry an explicit retention signal.
Potential savings
$10 to $1,800 / month
$120 to $21,600 / year
Detector ID
gcp-persistent-disk-unattached
GCP spend can compound through retention settings, storage choices, and low-visibility defaults, so the waste pattern is often hidden inside a configuration that still looks technically valid.
Use the hub when you know the platform but not the exact issue yet, or when you want to understand which related detectors belong in the same review cycle.
No. They are also useful for data and platform teams who need to review BigQuery, GCS, and stale infrastructure cost behavior as part of normal product operations.
Early Access
Cloud Waste Hunter is being built to connect detector findings into one review flow with savings estimates, cleanup context, and practical next steps.
