How to Use Data for Cloud Financial Management

How I Cut Cloud Costs by $300K+ Annually: 3 Real FinOps Wins When leadership asked me to “figure out why our cloud bill keeps growing Here’s how I turned cost chaos into controlled savings: Case #1: The $45K Monthly Reality Check The Problem: Inherited a runaway AWS environment - $45K/month with zero oversight My Approach: ✅ 30-day CloudWatch deep dive revealed 40% of instances at <20% utilization ✅ Right-sized over-provisioned resources ✅ Implemented auto-scaling for variable workloads ✅ Strategic Reserved Instance purchases for predictable loads ✅ Automated dev/test environment scheduling (nights/weekends off) Impact: 35% cost reduction = $16K monthly savings Case #2: Multi-Cloud Mayhem The Problem: AWS + Azure teams spending independently = duplicate everything My Strategy: ✅ Unified cost allocation tagging across both platforms ✅ Centralized dashboards showing spend by department/project ✅ Monthly stakeholder cost reviews ✅ Eliminated duplicate services (why run 2 databases for 1 app?) ✅ Negotiated enterprise discounts through consolidated commitments Impact: 28% overall reduction while improving DR capabilities Case 3: Storage Spiral Control The Problem: 20% quarterly storage growth, 60% of data untouched for 90+ days in expensive hot storage My Solution: 1, Comprehensive data lifecycle analysis 2, Automated tiering policies (hot → warm → cold → archive) 3, Business-aligned data retention policies 4, CloudFront optimization for frequent access 5, Geographic workload repositioning 6, Monthly department storage reporting for accountability Impact: $8K monthly storage savings + 45% bandwidth cost reduction ----- The Meta-Lesson: Total Annual Savings: $300K+ The real win wasn’t just the money - it was building a cost-conscious culture** where: - Teams understand their cloud spend impact - Automated policies prevent cost drift - Business stakeholders make informed decisions - Performance actually improved through better resource allocation My Go-To FinOps Stack: - Monitoring: CloudWatch, Azure Monitor - Optimization: AWS Cost Explorer, Trusted Advisor - Automation: Lambda functions for policy enforcement - Reporting: Custom dashboards + monthly business reviews - Culture: Showback reports that make costs visible The biggest insight? Most “cloud cost problems” are actually visibility and accountability problems in disguise. What’s your biggest cloud cost challenge right now? Drop it in the comments - happy to share specific strategies! 👇 FinOps #CloudCosts #AWS #Azure #CostOptimization #DevOps #CloudEngineering P.S. : If your monthly cloud bill makes you nervous, you’re not alone. These strategies work at any scale.

Gaining the right visibility on your cloud spend starts with bridging the gap between expectation and reality, and asking the right questions. Let me explain: Imagine this: Your Dev and QA spend is 60% of your bill, while Production is 40%. Your CFO makes a budget forecast based on what other companies do and models it as 70% Production and 30% Dev and QA. The numbers might differ, but the point still stands. The problem isn’t just overspending. It’s the disconnect between expectation and reality. Here’s how to bridge that gap: 1) Visibility begins by asking the toughest questions: - Why is production only 40% of our costs when we modeled it at 70%? - Why is Dev and QA double what we expected – from 30% to 60%? Tough questions surface the disconnect and provide clarity. Maybe Dev and QA are temporarily higher due to R&D for a new product launch. Or maybe it’s inefficiency that requires tighter environments. Either way, the right questions drive trust in your data and guide the next steps. 2) Map costs dynamically To understand where your money is going, you need dynamic cost attribution – by team, application, or cost center. The data you need is often scattered: half-baked tag resources, hierarchies in systems like Workday or ServiceNow, etc. A good cost-attribution engine like Yotascale pulls it all into one place, making it easy to identify who or what is driving your spending. Once you trust your data, you can start asking the right questions and then act. 3) Forecast proactively No one wants to get called into the CEO’s office because of an unexpected 400% budget overshoot. And that’s *exactly* why proactive forecasting is important. Forecast spend daily to catch spikes before they happen. For example: - Application A has a $150K budget but shoots up to $900K. - Your tools should flag this ahead of time so you can adjust before a crisis hits. This also lets you plan for fluctuations, e.g., higher costs this month due to R&D but a steady decline after launch. The key is setting guardrails and keeping tabs consistently.

It has been interesting few weeks diving into Cloud Cost Forecasting. We learned quite a few things as we build this out! This is how we are approaching, would love insights from the community: To truly build driver-based forecasting we are considering historical trends and forecast adjustment that considers cloud migration schedule, cost optimization impact and more. 0. We captured "Actual Spend" from Billing data. 1. We defined the term "Expected Spend" as "ML Generated Forecast" + "Forecast Adjustment" based on future business drivers. 2.1. We used "Forecast Adjustment" through simplified estimation template and plain old automation that connect Google Sheets with BigQuery and Looker. This allows real time changes to flow into reporting and also our friends in Finance love this :) 2.2. Then we used Machine Learning model to specifically train cost usage and billing data for past XX months and tune 15+ hyper-parameters to adjust trends catering to each workloads. We then automated training ML models to avoid any data drift and context drift. 3. We then calculated variance between the "Actual Spend" and "Expected Spend". Positive variance are then reported to further open up conversation with FinOps and Application team to understand more. Variance could be from inefficiency in architecture or simply because of new product or feature release that serves more customer effectively. The goals is to be aware about whats happening. 4. We then compare "Actuals + Expected spend" with the Budget to determine budget variance. This allows teams to course correct and take action sooner. If you are going over budget, how can your team adapt to this? If you are under budget, how can you fuel more innovation? 5. And finally use AI to have natural language conversation with data. We call this Dr. Bullseye Forecaster :D Checkout the iteration below and let us know your comments on what else can we consider in forecasting cloud spend. Thanks to Matthew Orr Lyndsey Pileggi Eric F. Eric Lam Bruce Warner Andre Ellis Jr. David Dinh for helping throughout the solution ❤️. #cloudcostforecasting #generativeAI #gemini #BigQuery #machinelearning #cloudfinops #cloudfinancialmanagement

Your cloud budget is never more vulnerable than the hours it spends locked inside Excel. Microsoft's finance team understood this when they replaced a seven-day spreadsheet cycle with a one-hour ML run that delivered 99% revenue forecast accuracy. The faster signal let them secure capacity discounts the same day and freed analysts for strategic work instead of cell updates. Cloud economics move too fast for monthly budget cycles. AI workloads surge overnight, Reserved-instance windows close within days,  Missed commitments erode margins before finance teams can react. Real-time forecasts convert spending volatility into negotiating power because decisions rely on current data rather than trailing averages. You can measure this delay cost directly. Count the days between usage and actionable insight, then multiply by the average daily cloud spend.    Many teams discover that every 24-hour lag adds thousands to the bill. Once you see that figure, the blind spot becomes obvious, and closing it starts compounding leverage month after month. I break down the data inputs and purchase timing that make this model work in this week's newsletter. Link is in the bio.

As I work with business leaders, the challenge is constant: how do we continue to invest in cutting-edge capabilities like AI to drive growth, while also maintaining fiscal discipline? What they need, and what I'm focused on, is a well-defined plan for financial resilience – and cloud spend is a prime area for optimization. I'm seeing firsthand how the very AI we're so excited to deploy is becoming one of the best helpers for financial efficiency we've encountered. Here are three key strategies I'm seeing companies adopt to manage economic uncertainty and maximize their AI investments: AI-Powered Forecasting: We're moving away from rigid, historical-data-only forecasts. I'm helping organizations adopt hybrid approaches that combine traditional models with adaptive AI, like the pre-trained TimesFM model from Google Research, for highly accurate predictions and anomaly detection. AI for Cost Optimization: Trying to make sense of millions of billing entries across tens of thousands of SKUs is a monumental task. I've seen how AI pattern recognition, like that in Gemini Cloud Assist, can cut through this complexity, identifying unexpected cost optimization opportunities that traditional analysis often misses. AI-Driven Financial Guardrails: It's all about proactive management. We're helping customers implement AI-driven cost anomaly detection solutions to continuously monitor cloud spending and avoid surprises. This ensures we're managing spend effectively while still supporting multi-cloud capabilities. For those just starting, three key initial steps: start with visibility (tagging!), deploy basic alerting, and establish an iterative process with a feedback loop. The future of AI and FinOps is rapidly evolving, with AI agents poised to revolutionize cloud cost management. How are you preparing for this shift? https://lnkd.in/eF3zVct3 #AI #FinOps #CloudCostManagement #GoogleCloud #DigitalTransformation #GoogleCloudConsulting #AIforFinOps