Avoiding Missteps in Month-over-Month Cloud Spend Analysis
Every March, FinOps teams notice a familiar trend: AWS cloud costs look higher than in February. Alarms go off. Slack threads light up. Engineers are asked to investigate usage spikes. But often, there’s no actual inefficiency — just a calendar quirk that gets overlooked.
The Problem: Misleading Comparisons
It’s easy to fall into the trap of comparing March spend to February and assuming something went wrong. But here’s the catch:
- February has only 28 or 29 days.
- March has 31 days.
That’s a 10.7% increase in time — more hours to run workloads, more days to accumulate storage costs, more time for on-demand resources to tick away. If your usage patterns are consistent, you should expect a higher bill in March.
The Insight: Time Skews Spend
Month-over-month cost analysis without accounting for time is fundamentally flawed.
Cloud bills are a function of time-based consumption. A longer month means more hours for EC2 instances, EBS volumes, and Lambda invocations to run — even if nothing else changes.
This is why absolute spend comparisons can mislead stakeholders. They might think spend is increasing due to inefficiencies, but often it’s just the month being longer.
The FinOps Fix: Normalize for Time
Use 730 Hours as a Standard Baseline
One solution is to normalize cloud costs using a time-based metric. FinOps teams often use 730 hours per month as a standard baseline (365 days ÷ 12 × 24 = 730). This smooths out month-length differences and allows for cleaner trend analysis.
Better Metrics:
- Cost per hour
- Cost per day
- Normalized monthly cost = total cost ÷ actual hours × 730
These are more reliable indicators of real usage shifts and efficiency changes.
Technical Example: Normalizing in Athena
If you’re working with AWS Cost and Usage Reports (CUR) in Athena, you can normalize costs using SQL. Here’s a simple query that calculates cost per day and normalized monthly cost:
SELECT
year,
month,
SUM(line_item_blended_cost) AS total_cost,
COUNT(DISTINCT DATE_TRUNC('day', line_item_usage_start_date)) AS days_in_month,
SUM(line_item_blended_cost) / COUNT(DISTINCT DATE_TRUNC('day', line_item_usage_start_date)) AS cost_per_day,
SUM(line_item_blended_cost) / (COUNT(DISTINCT DATE_TRUNC('day', line_item_usage_start_date)) * 24) * 730 AS normalized_monthly_cost
FROM
aws_cur_table
WHERE
product_product_code = 'AmazonEC2' -- or any service filter
AND year = 2024
GROUP BY
year, month
ORDER BY
year, month;This approach helps identify real cost changes versus calendar-driven fluctuations.
Reporting Recommendations
To make this actionable in your reporting:
- Include normalized metrics like cost per day or per hour alongside total cost.
- Visualize both raw and normalized values in charts — bar graphs with dual axes work well.
- Educate stakeholders on why March costs more than February, even when nothing changed.
This isn’t just a reporting tweak — it’s a FinOps best practice for accurate budgeting, forecasting, and anomaly detection.
Optional Visualization
Consider a chart like this in your dashboard:
| Month | Raw Spend | Normalized Cost (730 hrs) |
|---|---|---|
| February | $31,000 | $33,500 |
| March | $34,000 | $33,600 |
This shows that while the March bill is higher, normalized cost is nearly flat — telling a much more accurate story.
Call to Action: Make Normalization the Default
If you’re using Grafana, Looker Studio, Excel, or any other BI tool for FinOps, start adding normalized cost metrics into your dashboards. Set the expectation that trends should be viewed through the lens of time, not just dollars.
It’s a small shift, but it saves time, avoids miscommunication, and gives a clearer picture of what’s really happening in your cloud environment.
