Forum Discussion
Understanding Cloud Cost Fluctuations with Power BI
Staying on top of your cloud costs requires regular reviews. There are many ways to slice and dice your cloud costs; one approach I find helpful is comparing daily and monthly cost deltas.
Below is a visual from my Power BI report showing how my previous month’s costs compare to the month prior. The visual is filtered to only show delta increases/decreases over $1K. I can quickly see we spent $5K more on Azure SQL Database in the selected month compared to the previous month. I call this my 'large cost swings' graph.
I understand that everything is not linear, nor do things translate nicely from one day or month to the next. However, the data has a story to tell. What I ask my team to focus on is the story the data is telling. In this case, we made some modifications to ADF and SQL, leading to a $4K net reduction in costs.
Some stories explain the outcome of one or more actions. Then there are those stories which can help shape your future consumption and spending.
9 Replies
How can I get this PowerBi Report?
- micflan
Microsoft
This looks nice. Would you be interested in contributing something like this into the FinOps toolkit?
Power BI docs @ https://aka.ms/ftk/pbi/docs
Repository @ https://aka.ms/ftk
- Best Practices for Cloud Cost Management with Power BI:
- Define Cost Metrics: Establish a clear set of cost metrics (e.g., total cost, service-wise breakdown, usage patterns) to ensure consistent tracking and analysis.
- Regular Reviews: Schedule regular reviews of your Power BI reports to monitor costs and detect unusual fluctuations early.
- Set Alerts: Power BI allows you to set up alerts based on threshold values, such as when costs increase by more than $1K in a given period, so you can respond to cost changes promptly.
- Cross-functional Collaboration: Use the insights gathered to collaborate with teams (e.g., cloud architects, developers) to optimize usage and improve cost efficiency.
- Delta Analysis: By comparing cost deltas (the differences between current and past costs), you can identify patterns or anomalies. For instance, in your Power BI report, filtering for costs that have increased or decreased by over $1K helps you quickly identify significant changes. This makes it easier to target areas where cost fluctuations are substantial and require attention.
- Identifying Large Cost Swings: The visual highlighting large cost swings, such as the $5K increase in Azure SQL Database costs, serves as an early warning sign for further investigation. These swings may not always be the result of specific actions but can indicate changes in usage patterns or resource consumption, helping you to focus on high-impact areas first.
- Linking Costs to Specific Actions: As you observed, changes in Azure Data Factory (ADF) and SQL configurations led to a $4K net reduction in costs. This illustrates the importance of being able to connect cost changes to specific actions taken, which is essential for making data-driven decisions on how to optimize future resource usage and spending.
- Storytelling with Data: Beyond just identifying where costs have gone up or down, the goal should be to understand the "why" behind the numbers. The story your data tells could be about actions taken to optimize, such as modifying ADF and SQL configurations. Alternatively, it could indicate areas where more attention is needed to control costs, such as unused resources or scaling issues.
- Predictive Analysis for Future Spending: By reviewing cost trends and fluctuations, you can forecast future spending patterns. This helps anticipate any further spikes in costs and develop strategies to prevent them. In Power BI, using features like forecasting and trend lines can offer insights into how your cloud costs are likely to evolve, guiding you to make adjustments before costs spiral out of control.
