Cloud SQL instances are often over-provisioned or left running despite low utilization. Since billing is based on allocated vCPUs, memory, and storage — not usage — any misalignment between actual workload needs and provisioned capacity leads to unnecessary spend. Common causes include: * Initial oversizing during launch that was never revisited * Non-production environments with continuous uptime but minimal use * Databases used intermittently (e.g., for nightly reports) but kept running 24/7 Without rightsizing or scheduling strategies, these instances generate ongoing cost with limited business value.
In Cloud Run, each revision is deployed with a fixed memory allocation (e.g., 512MiB, 1GiB, 2GiB, etc.). These settings are often overestimated during initial development or copied from templates. Unlike auto-scaling platforms that adapt instance size based on workload, Cloud Run continues to bill per the allocated amount regardless of actual memory used during execution. If a service consistently uses significantly less memory than allocated, it results in avoidable overpayment per request — especially for high-throughput or long-running services. Since memory and CPU are billed together based on configured values, this inefficiency compounds quickly at scale.
Each Cloud NAT gateway provisioned in GCP incurs hourly charges for each external IP address attached, regardless of whether traffic is flowing through the gateway. In many environments, NAT configurations are created for temporary access (e.g., one-off updates, patching windows, or ephemeral resources) and are never cleaned up. If no traffic is flowing, these NAT gateways remain idle yet continue to generate charges due to reserved IPs and persistent gateway configuration. This is especially common in non-production environments or when legacy configurations are forgotten.
Node pools provisioned with large or specialized VMs (e.g., high-memory, GPU-enabled, or compute-optimized) can be significantly overprovisioned relative to the actual pod requirements. If workloads consistently leave a large portion of resources unused (e.g., low CPU/memory request-to-capacity ratio), the organization incurs unnecessary compute spend. This often happens in early cluster design phases, after application demand shifts, or when teams allocate for peak usage without autoscaling.
Cloud Memorystore instances that remain idle—i.e., not receiving read or write requests—continue to incur full costs based on provisioned size. In test environments, migration scenarios, or deprecated application components, Redis instances are often left running unintentionally. Since Redis does not autoscale or suspend, unused capacity results in 100% waste until explicitly deleted.
By default, Cloud Logging retains logs for 30 days. However, many organizations increase retention to 90 days, 365 days, or longer — even for non-critical logs such as debug-level messages, transient system logs, or audit logs in dev environments. This extended retention can lead to unnecessary costs, especially when: * Logs are never queried after the first few days * Observability tooling duplicates logs elsewhere (e.g., SIEM platforms) * Retention settings are applied globally without considering log type or project criticality
Pub/Sub Lite is a cost-effective alternative to standard Pub/Sub, but it requires explicitly provisioning throughput capacity. When publish or subscribe throughput is overestimated, customers continue to pay for unused capacity — similar to idle virtual machines or overprovisioned IOPS. This inefficiency is often found in development environments or early-stage production workloads where traffic patterns are unpredictable or have since decreased.
Even when no user workloads are active, GKE Autopilot clusters continue running system-managed pods that accrue compute and storage charges. These include control plane components and built-in agents for observability and networking. If Autopilot clusters are deployed in non-production or experimental environments and left idle, they may silently accrue ongoing charges unrelated to application activity. This inefficiency often occurs in: * Dev/test clusters that are spun up temporarily but not deleted * Clusters used for one-time jobs or training workloads * Scheduled workloads that run infrequently but don't trigger downscaling
When GCS object versioning is enabled, every overwrite or delete operation creates a new noncurrent version. Without a lifecycle rule to manage old versions, they persist indefinitely. Over time, this results in: * Accumulation of outdated data * Unnecessary storage costs, especially in Standard or Nearline classes * Lack of visibility into what is still needed vs. legacy debris This issue often goes unnoticed in environments with frequent data updates or automated processes (e.g., logs, models, config snapshots).
If a table is not partitioned by a relevant column (typically a timestamp), every query scans the entire dataset, even if filtering by date. This leads to: * High costs per query * Long execution times * Inefficient use of resources when querying recent or small subsets of data This inefficiency is especially common in: * Event or log data stored in raw, unpartitioned form Historical data migrations without schema optimization * Workloads developed without awareness of BigQuery’s scanning model
