When the EC2 instance types used for EKS node groups have a memory-to-CPU ratio that doesn’t match the workload profile, the result is poor bin-packing efficiency. For example, if memory-intensive containers are scheduled on compute-optimized nodes, memory may run out first while CPU remains unused. This forces new nodes to be provisioned earlier than necessary. Over time, this mismatch can lead to higher compute costs even if the cluster appears fully utilized.
S3 buckets often persist after projects complete or when the associated workloads have been retired. If a bucket is no longer being read from or written to—and its contents are not required for compliance, backup, or retention purposes—it represents ongoing cost without delivering value. Many organizations overlook these idle buckets, especially in shared or legacy accounts, leading to unnecessary storage costs over time.
Some ElastiCache clusters continue to run on older-generation node types that have since been replaced by newer, more cost-effective options. This can happen due to legacy templates, lack of version validation, or infrastructure that has not been reviewed in years. Newer instance families often deliver better performance at a lower hourly rate. Modernizing to newer node types can reduce compute spend without sacrificing performance, and in many cases, improve it.
Workloads are sometimes deployed in specific AWS regions based on legacy decisions, developer convenience, or perceived performance requirements. However, regional EC2 pricing can vary significantly, and placing instances in a suboptimal region can lead to higher compute costs, increased data transfer charges, or both. In particular, workloads that frequently communicate with resources in other regions—or that serve a user base concentrated elsewhere—can incur unnecessary costs. Re-evaluating regional placement can reduce these costs without compromising performance or availability when done strategically.
Some architectures unintentionally route large volumes of traffic between resources that reside in different Availability Zones—such as database queries, service calls, replication, or logging. While these patterns may be functionally correct, they can lead to unnecessary data transfer charges when the traffic could be contained within a single AZ. Over time, this can become a silent cost driver, especially for chatty microservices, replicated storage layers, or high-throughput pipelines. Re-architecting for AZ-locality—when possible—can reduce these charges without affecting availability in environments where high resilience isn’t required.
Some S3 lifecycle policies are configured to transition objects from Standard storage to Intelligent-Tiering after a fixed number of days (e.g., 30 days). This creates a delay where objects reside in S3 Standard, incurring higher storage costs without benefit. Since Intelligent-Tiering does not require prior access history and can be used immediately, it is often more efficient to place objects directly into Intelligent-Tiering at the time of upload. Lifecycle transitions introduce unnecessary intermediate costs that can be avoided entirely through configuration changes.
VPC Interface Endpoints are commonly deployed to meet network security or compliance requirements by enabling private access to AWS services. However, these endpoints often remain provisioned even after the original use case is deprecated. In some cases, the applications have been decommissioned; in others, traffic routing has changed and the endpoint is no longer used. Since interface endpoints generate hourly charges whether or not they are used, identifying and removing inactive ones can eliminate unnecessary costs.
Manual snapshots are often created for operational tasks like upgrades, migrations, or point-in-time backups. Unlike automated backups, which are automatically deleted after a set retention period, manual snapshots remain in place until explicitly deleted. Over time, this can lead to an accumulation of snapshots that are no longer needed but still incur monthly storage charges. This is particularly common in environments where snapshots are taken frequently but not consistently reviewed. If left unmanaged, manual snapshots can become a source of ongoing cost, especially for large databases or when snapshots are copied across regions.
GCP VM instances are often provisioned with more CPU or memory than needed, especially when using custom machine types or legacy templates. If an instance consistently consumes only a small portion of its allocated resources, it likely represents an opportunity to reduce costs through rightsizing. Without proactive reviews, these oversized instances can remain unnoticed and continue to incur unnecessary charges.
NAT Gateways are convenient for enabling outbound access from private subnets, but in data-intensive environments, they can quietly become a major cost driver. When large volumes of traffic flow through the gateway—particularly during batch processing, frequent software updates, or hybrid cloud integrations—the per-GB charges accumulate rapidly. In some cases, replacing a managed NAT Gateway with a self-managed NAT instance can substantially reduce costs, provided that the organization is prepared to operate and maintain the alternative solution.
