Cloud Run allows users to allocate up to 8 GB of memory per container instance. If memory is overestimated — often as a buffer or based on unvalidated assumptions — customers pay for more than what the workload consumes during execution. Unlike in VM-based environments where memory might be shared or underutilized without direct cost impact, in Cloud Run, you're billed precisely for what you allocate. This inefficiency often results from: * Defaulting to high memory values for “safety” * Not using monitoring tools to assess actual memory usage * Lack of clear ownership over service tuning
Teams often adopt flat-rate pricing (slot reservations) to stabilize costs or optimize for heavy, recurring workloads. However, if query volumes drop — due to seasonal cycles, architectural shifts (e.g., workload migration), or inaccurate forecasting — those reserved slots may sit underused. This inefficiency is easy to miss, as the cost remains fixed and detached from usage volume. Unlike autoscaling models, reservations require active monitoring and manual adjustment. In some organizations, multiple projects reserve separate slot pools, exacerbating waste through fragmentation.
Cloud Functions scale to zero when idle. When invoked after inactivity, they undergo a "cold start," initializing runtime, loading dependencies, and establishing any required network connections (e.g., VPC connectors). These cold starts can dramatically increase execution time, especially for functions with: * High memory allocations * Heavy initialization logic * VPC connector requirements If cold starts are frequent, customers may be paying for unnecessary compute time — particularly in latency-sensitive workloads — without receiving proportional value.
When EC2 instances are provisioned, each attached EBS volume has a `DeleteOnTermination` flag that determines whether it will be deleted when the instance is terminated. If this flag is set to `false` — often unintentionally in custom launch templates, AMIs, or older automation scripts — volumes persist after termination, resulting in orphaned storage. While detached volumes are easy to detect and clean up after the fact, proactively identifying attached volumes with `DeleteOnTermination=false` can prevent future waste before it occurs.
While moving objects to colder storage classes like Glacier or Infrequent Access (IA) can reduce storage costs, premature transitions without analyzing historical access patterns can lead to unintended expenses. Retrieval charges, restore time delays, and early delete penalties often go unaccounted for in simplistic tiering decisions. This inefficiency arises when teams default to colder tiers based solely on perceived “age” of data or storage savings—without confirming access frequency, restore time SLAs, or application requirements. Unlike inefficiencies focused on *underuse* of cold storage, this inefficiency reflects *overuse* or misalignment, resulting in higher total costs or operational friction.
VM-based Committed Use Discounts in GCP offer cost savings for predictable workloads, but they are rigid: they apply only to specified VM types, quantities, and regions. When organizations evolve their architecture — such as moving to GKE (Kubernetes), Cloud Run, or autoscaling — usage patterns often shift away from the original commitments. Because GCP lacks flexible reallocation options like AWS Convertible RIs or Savings Plans, underutilized commitments lead to sustained, silent waste. This is especially common when workload changes go uncoordinated with finance or centralized planning.
Many environments continue using io1 volumes for high-performance workloads due to legacy provisioning or lack of awareness of io2 benefits. io2 volumes provide equivalent or better performance and durability with reduced cost at scale. Failing to adopt io2 where appropriate results in unnecessary spend on IOPS-heavy volumes.
When large numbers of objects are deleted from S3—such as during cleanup or lifecycle transitions—CloudTrail can log every individual delete operation if data event logging is enabled. This is especially costly when deleting millions of objects from buckets configured with CloudTrail data event logging at the object level. The resulting volume of logs can cause a significant, unexpected spike in CloudTrail charges, sometimes exceeding the cost of the underlying S3 operations themselves. This inefficiency often occurs when teams initiate bulk deletions for cleanup or cost savings without realizing that CloudTrail logs every API call, including `DeleteObject`, if data event logging is active for the bucket.
Non-production OpenSearch domains often inherit Multi-AZ configurations from production setups without clear justification. This leads to redundant replica shards across AZs, inflating both compute and storage costs. Unless strict uptime or fault tolerance requirements exist, most dev/test workloads do not benefit from Multi-AZ redundancy.
Recursive invocation occurs when a Lambda function triggers itself directly or indirectly, often through an event source like SQS, SNS, or another Lambda. This loop can be unintentional — for example, when the function writes output to a queue it also consumes. Without controls, this can lead to runaway invocations, dramatically increasing cost with no business value.
