By default, EventBridge includes retry mechanisms for delivery failures, particularly when targets like Lambda functions or Step Functions fail to process an event. However, if these retry policies are disabled or misconfigured, EventBridge may treat failed deliveries as successful, prompting upstream services to republish the same event multiple times in response to undelivered outcomes. This leads to: * Duplicate event publishing and delivery * Unnecessary compute triggered by repeated events * Increased EventBridge, downstream service, and data transfer costs This behavior is especially problematic in systems where idempotency is not strictly enforced and retries are managed externally by upstream services.

By default, CloudWatch Log Groups use the Standard log class, which applies higher rates for both ingestion and storage. AWS also offers an Infrequent Access (IA) log class designed for logs that are rarely queried — such as audit trails, debugging output, or compliance records. Many teams assume storage is the dominant cost driver in CloudWatch, but in high-volume environments, ingestion costs can account for the majority of spend. When logs that are infrequently accessed are ingested into the Standard class, it leads to unnecessary costs without impacting observability. The IA log class offers significantly reduced rates for ingestion and storage, making it a better fit for logs used primarily for post-incident review, compliance retention, or ad hoc forensic analysis.

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.

Search Optimization can enable significant cost savings when selectively applied to workloads that heavily rely on point-lookup queries. By improving lookup efficiency, it allows smaller warehouses to satisfy performance SLAs, reducing credit consumption.

However, inefficiencies arise when:

• Search Optimization is not enabled on critical lookup-heavy tables, forcing oversized warehouses.
• It is enabled unnecessarily on infrequently queried data, adding avoidable costs.
• Warehouse sizing is not adjusted after Search Optimization is implemented, missing the primary cost-saving opportunity.

Regular review of query patterns and warehouse sizing is essential to maximize the intended benefit of Search Optimization.

Inefficient pipeline refresh scheduling occurs when data refresh operations are executed more frequently, or with more compute resources, than the actual downstream business usage requires.

Without aligning refresh frequency and resource allocation to true data consumption patterns (e.g., report access rates in Tableau or Sigma), organizations can waste substantial Snowflake credits maintaining underutilized or rarely accessed data assets.

Inefficiency arises when MVs are either underused or misused.

• When high-cost, repetitive queries are not backed by MVs, workloads consume unnecessary compute resources.
• When MVs exist but are rarely queried, their background refresh and storage costs accumulate without offsetting savings.

Proper evaluation of workload patterns and strategic use of MVs is critical to achieve a net cost benefit.

Organizations may experience unnecessary Snowflake spend due to inefficient query-to-warehouse routing, lack of dynamic warehouse scaling, or failure to consolidate workloads during low-usage periods. Third-party platforms offer solutions to address these inefficiencies:

• Sundeck enables highly customizable, SQL-based control over the query lifecycle through user-defined rules (Flows, Hooks, Conditions). Cost optimization techniques include adaptive warehouse routing, instant warehouse suspension, and off-peak consolidation. However, it requires users to maintain optimization logic manually.
• Keebo offers a fully automated AI-driven approach, dynamically tuning warehouse size, clustering, and memory configurations without requiring manual query intervention. It prioritizes minimal operational effort with continuous background optimization.

Choosing between these solutions depends heavily on the organization's internal capabilities and desired balance between control and automation.

Excessive Auto-Clustering costs occur when tables experience frequent and large-scale modifications ("high churn"), causing Snowflake to constantly recluster data. This leads to significant and often hidden compute consumption for maintenance tasks, especially when table structures or loading patterns are not optimized. Poor clustering key choices, unordered data loads, or frequent full-table replacements are common drivers of unnecessary Auto-Clustering activity.

Ingesting a large number of small files (e.g., files smaller than 10 MB) using Snowpipe can lead to disproportionately high costs due to the per-file overhead charges. Each file, regardless of its size, incurs the same overhead fee, making the ingestion of numerous small files less cost-effective. Additionally, small files can increase the load on Snowflake's metadata and ingestion infrastructure, potentially impacting performance.