Hourly Time Aggregation
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Last updated
Hourly Data aggregation provides detailed cost visibility at an hourly granularity for AWS data, enabling teams to:
Investigate at a granular level - Uncover short-term spikes and irregular usage patterns:
Detect cost anomalies that are hidden in daily views.
Align monitoring with the real-time behavior of systems, which often follow an hourly cycle.
Improve root cause analysis by narrowing down the time and component affected.
Optimize cloud usage – Detect inefficiencies at the hourly level for improved cost control and performance.
Validate budget and forecast accuracy – Ensure cost fluctuations align with expected usage patterns.
Track Kubernetes costs with precision – Understand workload-level spending trends in AWS Kubernetes (K8s).
Which cloud providers currently support hourly data insights?
Currently, hourly data insights are available for AWS services and AWS Kubernetes (K8s). Support for additional cloud providers, such as GCP, is coming soon.
What is the optimal timeframe for viewing hourly data?
To ensure optimal performance and accuracy, MegaBill displays hourly data for up to 14 consecutive days. If a longer period is selected, the latest 14 days within that period will be shown. For example, selecting March 1 – March 20 will display hourly data from March 7 – March 20.
How far back can I view hourly data insights?
Hourly-level insights are available for the most recent 180 days. Beyond this period, you can analyze trends using daily or monthly aggregations. For example, if you need to review hourly trends from October 2023 and today is March 2025, hourly data for that period will no longer be available.
Can I use hourly data for trend projections and computational layers?
Currently, hourly time aggregation is not available for trend projections and computational layers.
Are Virtual Tags supported with hourly aggregation? Yes, virtual tags are supported with hourly aggregation. However, virtual tags with reallocation are not supported, as reallocation metrics are based on daily data.
