Unlocking Granular Visibility into AWS Spending with Cloud Financial Management

As AWS cloud usage grows across large enterprises, gaining meaningful visibility into what services and specific resources are driving spend becomes increasingly difficult. Traditional AWS cost tools like Cost Explorer provide high-level views that lack the precise, granular insights needed to truly understand and optimize expenses.

Addressing this gap, AWS’s new Cloud Financial Management service applies advanced analytics and visualization to interrogate cloud spending from multiple perspectives. By breaking down AWS expenses across customizable dimensions, organizations can shine a light into previously dark corners of the bill to find and fix inefficiencies.

Centralized and Detailed AWS Usage Reporting

While Cost Explorer displays overall AWS percentages by service, Cloud Financial Management offers detailed filtering on usage types, resource IDs, tags, availability zones, and more. Easily construct usage reports for precise insights, like analyzing EC2 costs by instance family, size, lifecycle, and tenancy.

Dashboards can also be customized to focus usage views by departments, environments, applications and other attributes using tags. Isolate production system spending from development and test resources. Break out usage by geographic region or AWS account to match organizational or billing hierarchies.

Analysis by Time Components

In addition to usage-based detail, expenses can be analyzed across time to identify trends or anomalies. View spend by day, week or month. Spot odd usage spikes or dips to investigate further. Compare weeks over weeks or month-over-month to surface seasonal usage patterns tied to promotions, campaigns or holiday traffic.

Granular usage reporting down to the hour allows you to analyze consumption over the course of a day. When does resource usage peak and plummet? Identify low-use overnight hours as an opportunity to save on operating expenses through shutdown.

Recommendations to Realize Savings
Applying machine learning algorithms against granular historical usage and cost data sets, Cloud Financial Management can pinpoint specific areas to target savings, like:

• Overprovisioned resource waste that should scale down to meet actual demand
• Idle resources with low utilization still accruing charges
• Reserved Instance & Saving Plan purchase recommendations based on steady-state resource patterns

Detailed visibility unlocks targeted cost optimization opportunities previously hidden at the aggregate view level. Finance leaders can extend cloud visibility and then take informed actions to eliminate waste or improve spending efficiency. Cloud Financial Management transforms raw cloud data into clear financial intelligence needed to guide better cloud spending habits.