5 Case Studies of Real FinOps
Learn how to save from real experiences of cloud engineers
Victor Garcia
September 21, 2025
Together with Kion
Learn Why the Future of FinOps is deeply connected to CloudOps
Discover how aligning automation, policy, and governance drives greater efficiency, cost control, and innovation across multicloud environments.
Empower teams to move from reactive cost tracking to proactive financial and operational excellence.
ENGINEERING
5 Case Studies to Learn about Real FinOps
A developer who learned the hard way about cloud costs shares five real cases where AWS bills spiraled out of control and how to prevent them.
After getting hit with a $200 AWS surprise as a student, the author became obsessed with tracking cloud cost patterns. Now when founders share their "$47K bill shock" stories, the patterns are clear and preventable.
Case 1: The Viral Content Disaster
Chris Short's site went from $23 monthly to $2,657 overnight when he shared a 13.7GB file that went viral. AWS charged for every byte downloaded by every user worldwide. The lesson: data transfer costs can be 10 times more expensive than the servers hosting your content.
Case 2: The Auto-Scaling Death Loop
A startup's app had a memory leak causing CPU spikes every few hours. Auto-scaling launched 450+ new instances over 30 days, each billed for full hours even with 10-minute lifespans. Monthly damage: $18,400 for computing power that solved nothing.
Case 3: The Cross-Region Database Mistake
A team put their database in Virginia for cheaper storage but ran app servers in Oregon to be "closer to users." Every database query crossed regions, creating a $12,800 monthly surprise. Cross-region data transfer within the same cloud provider still costs serious money.
Case 4: The Forgotten Development Environments
A growing company spun up dev and staging environments for each feature branch and client demo. After six months, they had 47 active environments running 24/7, wasting $8,900 monthly on resources used only 2-3 hours weekly.
Case 5: The Load Balancer Multiplication
A startup created separate load balancers for each microservice, environment, and region without understanding costs. Fifteen load balancers at $23 base cost plus $150-300 capacity units each totaled $2,800+ monthly just for traffic routing.
Prevention Framework
The author recommends multi-cloud cost analysis before building, real-time cost monitoring instead of just alerts, and provider-agnostic architecture planning. Consider smaller providers like UpCloud or DigitalOcean that often include services the big providers charge for separately.
One documented case showed a startup moving from AWS to UpCloud, reducing monthly costs from $4,200 to $1,400 while maintaining performance through eliminated cross-region fees and simpler pricing.
CLOUD PROVIDERS
AWS CloudWatch Cross-Account Log Centralization & Azure European Data Transfer Fee Update
AWS
Amazon CloudWatch introduced cross-account log centralization, allowing free log copying to destination accounts. This simplifies management and reduces costs of custom solutions while providing centralized visibility across regions.
AWS Amazon S3 now supports conditional deletes using ETag values to prevent accidental data loss. This feature helps avoid costly recovery efforts by requiring matching ETags before delete operations succeed, reducing operational risk through bucket policy enforcement.
Amazon EC2 R8i and R8i-flex instances with Intel Xeon 6 processors deliver up to 15% better price-performance for memory-intensive workloads. R8i-flex instances offer additional savings for applications that don't fully utilize compute resources.
AWS Backup for S3 now supports selective metadata inclusion (ACLs and ObjectTags), helping optimize backup storage costs by including only necessary metadata rather than defaulting to everything.
Microsoft Azure
Microsoft Azure Azure data transfer now offers at-cost pricing for European customers moving data between Azure and other cloud providers in interoperable scenarios. Eligible organizations can request refunds through support tickets, making multi-cloud architectures more budget-friendly.
Google Cloud
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CLOUD PROVIDERS
Why Cloud needs to be a Public Utility
Here's the problem: just three companies control nearly two-thirds of all cloud services worldwide. Amazon Web Services, Microsoft Azure, and Google Cloud have become so powerful they can decide who gets access, how much it costs, and what rules everyone must follow.
These cloud giants play favorites with pricing. They give steep discounts to some customers while charging others full price. Microsoft gives OpenAI massive amounts of free computing power, while Amazon does the same for Anthropic.
Even worse, these companies can shut down services for political reasons. Amazon kicked Parler off its servers after January 6th.
Google and Amazon blocked Signal from helping people in authoritarian countries communicate securely. Private companies shouldn't have this much power over what information we can access.
When so few companies control such critical infrastructure, we're all at risk. If one of these giants goes down, millions of people lose access to essential services. It's like having only three companies control all the electricity in the country.
The authors argue we should treat cloud computing like a public utility, similar to electricity or water.
Public utilities are heavily regulated to ensure fair prices, reliable service, and equal access for everyone.
State utility commissions would oversee cloud providers, requiring transparent pricing and preventing discrimination.
This isn't a new idea. We did the same thing with railroads over a century ago when a few companies controlled all transportation and charged whatever they wanted.
Railroad regulation stopped price discrimination and secret deals that hurt small businesses and farmers. Cloud computing has become too important to our daily lives to let a few companies control it without oversight.
FINOPS EVENTS
The Hybrid FinOps Advantage
FinOps has expanded far beyond public cloud. Managing costs across data centers, AWS, Azure, SaaS, and AI workloads separately prevents total cost visibility and missed savings.
Discover how FinOps 2.0 strategies deliver comprehensive optimization across your entire technology portfolio.
You'll Learn How To:
Achieve total cost visibility across data centers, multi-cloud, SaaS, and AI infrastructure
Optimize the complete technology stack with unified intelligence and automation
Break down silos between FinOps, ITAM, procurement, and engineering teams
Speakers
Jeremy Chaplin, Gerhard Behr & Victor Garcia
November 23rd - 6:00 PM CEST / 10AM EST
BIGQUERY
Taming BigQuery Autoscaler: Cut Cloud Costs by 90%
Google BigQuery users often face a hidden cost problem with the autoscaler feature that can waste up to 90% of their spending without them knowing it.
The issue comes from BigQuery's billing model. When you use the capacity pricing option, Google charges you for a minimum of 60 seconds even if your query finishes in just 6 seconds. This creates what the author calls the "one-minute billing trap."
A quick 6-second query might use 1,000 slots to finish fast. You pay for 1,000 slots for 60 seconds, but the actual work only needed 6 seconds.
The fix is counter-intuitive. Instead of letting BigQuery use as many slots as it wants, you should limit the maximum slots in your reservation settings.
Using the same example, if you cap slots at 50, the query takes 2 minutes instead of 6 seconds. But now you only pay for slots that actually do work, cutting costs by 90%.
How to solve it:
Create separate reservations for different types of work. Use low slot limits for batch jobs that can run slower, and high limits for interactive tools that need speed.
Use BigQuery's built-in data tables to model your costs and find the sweet spot between speed and savings.
Test your new settings and measure the results. Adjust until you find the right balance. Keep monitoring and tuning as your data needs change over time.
GCP
Measure the value and impact of your AI Costs
Google Cloud experts have created a simple three-step plan to help businesses measure if their AI projects are actually helping or just costing money.
Step 1: Figure out what success looks like
Companies need to pick from four main ways AI can help their business:
Making work faster and cheaper by cutting down on manual tasks and fixing mistakes.
Growing revenue by speeding up new products or finding new ways to make money.
Making customers and workers happier with better experiences.
Step 2: Count all the costs
This means looking at everything you spend on AI, not just the monthly bills. You need to include training costs, computer power, and the people needed to keep everything running.
Step 3: Do the math
Take the money you save or make from step one and subtract what you spend from step two. This tells you how long it takes to pay back your investment and how much profit you'll make.
📺️ VIDEO
MCP tricks your CFO will Love
We delve into the integration of Model Context Protocol (MCP) and AI into FinOps practices together with Ben Schaechter (Vantage Co-Founder)
Discover how AI can automate cost analysis, reduce manual tasks, and make FinOps more accessible to various stakeholders within an organization.
🎖️ MENTION OF HONOUR
Guide: Backfill Azure Cost Management Exports (FOCUS-Ready)
Azure's cost management portal has a big problem for FinOps teams. It only shows 13 months of export history, but many organizations need years of data to build proper cost analysis and follow FOCUS standards.
Here's the good news: Azure CLI can grab all your historical cost data, not just the recent stuff.
What You Need First
You need the right permissions on your Azure account. Cost Management Reader role at minimum, plus Storage Blob Data Contributor for where your exports go.
You also need at least one export already set up in the Azure portal. This tells the system where to save your files.
The Simple Process
Open Azure Cloud Shell and install a helper tool called CostExportV2IngestData. Then run one command that walks you through picking your scope, export name, and date range. The tool breaks down your request into monthly chunks and runs each one.
Why This Matters for FOCUS
FOCUS is the standard way to format cloud cost data. If you're trying to build a complete data lake that follows FOCUS rules, you need all your historical information in the same format.
The portal's 13-month limit makes this impossible. But the CLI method lets you backfill everything and keep it consistent.
Real Results
After running this process, your storage account will have CSV or Parquet files for every month you requested. Even if the portal says it can't show data that old, the files will be there.
This method works across all your subscriptions, billing accounts, and enrollment scopes. You can run it for every export you have set up.
Professional Spotlight
Alexa Abbruscato
Queen of FinOps for Finance & Procurement
It’s great to see more people joining FinOps Weekly project, and we are super happy to have Alexa talking for the Finance FinOps side in her newsletter
That’s all for this week. See you next Sunday!
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