Tips for Cost-Effective Cloud Solutions

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.

Sharing some key learnings from my efforts to reduce cloud consumption costs for us and our customers using AI. Although AI helped speed up research, it did little in helping us in directly addressing the issue. We managed to find 40% savings in parts of our cloud infrastructure, leading to savings of >$10,000 per month without losing functionality by just spending 2 days on analysis. Here are my key takeaways: 1. Every expense should have an owner. If the CEO is the owner for many of these expenses, you are not delegating enough and can expect surprises. 2. Never lose track of expenses. 3. Know your workloads. Consolidating databases, changing lower environment clusters to zonal clusters, moving unused data to archival storage, stopping services we no longer use, and better understanding how we were getting charged for services were key drivers of costs. AI alone wouldn't be able to make these recommendations because it doesn't know the logical structure of your data, instances, databases, etc. 4. Review your processes to track and review expenses at least once a quarter. This is especially important for companies without a full-time CFO. Optimization is a continuous activity, and data is its backbone. Investing time and effort in consolidation, reporting, reviewing, and anomaly detection is critical to ensure you are running a tight ship. It's no longer just about top-line. The overall savings may not seem like a huge number, but it has a meaningful impact on our gross margins and that matters, a lot! Where do you start? - Go and ask that one question to your analyst you've been wanting to ask, but you have been putting it off. You never know what ROI you can get. #cloudcomputing #datawarehouse #dataanalysis #askingtherightquestions

In my last year at AWS, I was once tasked with finding $400 million in cost savings for cloud spending in just one year. It was a daunting challenge, but I learned a lot of valuable lessons along the way that I'd like to share with you. First, let's go over what I did to save that $400 million. Here are the top three strategies that worked for me: - Automation of idle instances: It's common for developers and testers to leave instances running even when they're not being used, which can add up quickly. We built automation to identify idle instances, tagged them, sent emails to people, and shut them down automatically if we didn’t get a response to leave them up. - Elimination of unused backups and storage: We found that we were keeping backups of customer data that we weren't using, which was costing us a lot of money. By reaching out to customers and getting their approval to delete backups that weren't being used, we were able to save a substantial amount of money. - Reserved instances: Reserved instances have a much lower cost than on-demand instances, so we made sure to buy them whenever possible. We also used convertible RIs so that we could shift between instance types if there were mispredictions about which types of instances would be in demand. Now, let's talk about what I would do differently if I were facing this challenge today. Here are two key strategies that I'd focus on: - Start with automation: As I mentioned earlier, automating the identification and shutdown of idle instances is crucial for cost savings. I'd make sure to start with this strategy right away, as it's one of the easiest and most effective ways to save money. - Be cautious with reserved instances: While RIs can be a great way to save money, they're not always the right choice. If you're in a world where you might be shrinking, not growing, you need to be much more cautious about buying RIs. Make sure to consider your commitment to buy and whether you'll be able to sell the capacity later. What would you add to this list? #devops #cloud #automation

𝗧𝗿𝗲𝗮𝘁 𝗙𝗶𝗻𝗢𝗽𝘀 𝗮𝘀 𝗮 𝘀𝗰𝗶𝗲𝗻𝗰𝗲 𝗳𝗼𝗿 𝗿𝗲𝗽𝗲𝗮𝘁𝗮𝗯𝗹𝗲 𝘀𝘂𝗰𝗰𝗲𝘀𝘀. Meet Varsha Sundar, VP of Global Cloud FinOps at Chubb and FinOps Foundation Ambassador. Having helped build and scale FinOps practices at Prudential Financial, Experian, and now Chubb, she's developed a scientific methodology that consistently delivers results - her first optimization project alone achieved $1.3M in annual savings. Listen now on:  Apple: https://lnkd.in/gUDAgJCT Spotify: https://lnkd.in/gSC7YsFt YouTube: https://lnkd.in/gK4xBjGc Sedai Website: https://lnkd.in/gQ5J_keM In our conversation, Varsha shares:  🔵 A step-by-step scientific framework for turning FinOps hypotheses into proven savings  🔵 The art of balancing performance requirements with cost optimization  🔵 How to effectively integrate both automated tools and human expertise in cloud management  🔵 Essential skills and practical experience needed for FinOps career success  🔵 The evolution of FinOps practices and tools in the industry  🔵 The potential of AI in cloud cost estimation and management Key Takeaways: 1️⃣ Treat every optimization like a scientific experiment. Start with a hypothesis, test in sandboxes, document your proof-of-concepts, and scale gradually from development to production. This methodical approach not only delivers better results but builds credibility with engineering teams. 2️⃣ Build proof before seeking buy-in. Start small, document detailed proof of concepts, understand stakeholder perspectives, and implement changes gradually. Your data and test results become your strongest allies in driving organizational change. 3️⃣ Success comes from merging science with practice. True FinOps mastery requires getting your hands dirty - running experiments, building business cases, and learning from real-world implementation. Theory alone isn't enough; you must combine rigorous methodology with practical experience. 4️⃣ The future of FinOps belongs to intelligent automation. Imagine AI systems that can instantly predict the cost implications of cloud migrations or proactively identify & capture optimization opportunities. This transformation will make cloud costs more transparent and predictable for teams transitioning from on-premises environments. #FinOps #CloudOptimization #CloudArchitecture #DevOps #GoAutonomous

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.

💵 Exploring the Design Principles of Cost Optimization in the 🔥Azure Well-Architected Framework🔥 Cost optimization is critical to ensuring your Azure workloads are not just effective but also efficient. By following the Azure Well-Architected Framework's cost optimization principles, businesses can maximize value while minimizing unnecessary expenses. Let’s break these principles down and see how they apply to Azure IaaS: 1️⃣ Develop Cost-Management Discipline Establish clear cost management practices, such as tagging resources, setting budgets, and implementing cost alerts. 💡 Example: Use Azure Cost Management and Billing to set up budgets for different resource groups and notify your team when you approach 80% of a budget limit. This avoids surprises and helps identify spending trends. 2️⃣ Design with a Cost-Efficiency Mindset Architect workloads to deliver the same or better performance at lower costs by choosing appropriate services and configurations. 💡 Example: For a workload needing VM redundancy, use Azure Availability Sets or Availability Zones rather than overprovisioning standalone VMs. This ensures high availability while keeping costs lower. 3️⃣ Design for Usage Optimization Optimize usage by understanding workload patterns and leveraging auto-scaling and scheduling to match resource demand. 💡 Example: Implement Azure Virtual Machine Scale Sets to automatically scale instances up or down based on demand, ensuring you only pay for the capacity you actually use. Additionally, schedule non-production environments (e.g., dev/test VMs) to shut down during non-working hours using Azure Automation. 4️⃣ Design for Rate Optimization Choose the most cost-effective pricing models, such as reserved instances or spot VMs, for workloads with predictable or interruptible usage. 💡 Example: Use Azure Reserved Virtual Machine Instances for workloads that run 24/7, like a production SQL Server VM, to achieve savings of up to 72% compared to pay-as-you-go pricing. For batch workloads, spot VMs provide significant cost savings. 5️⃣ Monitor and Optimize Over Time Cost optimization is not a one-time activity; it requires continuous monitoring and adjustments. 💡 Example: Regularly analyze your Azure Advisor recommendations to identify idle resources, overprovisioned VMs, or outdated configurations. For instance, rightsizing a VM that is underutilized from a Standard D4 to a D2 can lead to immediate savings. By applying these principles, organizations can align their Azure investments with business goals while staying efficient and agile. #Azure #CloudComputing #CostOptimization #AzureWellArchitected #AzureIaaS #CloudCostManagement #AzureTips #MicrosoftAzure #MicrosoftCloud #CloudArchitecture #AzureCost

It's astonishing that $180 billion of the nearly $600 billion on cloud spend globally is entirely unnecessary. For companies to save millions, they need to focus on these 3 principles — visibility, accountability, and automation. 1) Visibility The very characteristics that make the cloud so convenient also make it difficult to track and control how much teams and individuals spend on cloud resources. Most companies still struggle to keep budgets aligned. The good news is that a new generation of tools can provide transparency. For example: resource tagging to automatically track which teams use cloud resources to measure costs and identify excess capacity accurately. 2) Accountability Companies wouldn't dare deploy a payroll budget without an administrator to optimize spend carefully. Yet, when it comes to cloud costs, there's often no one at the helm. Enter the emerging disciplines of FinOps or cloud operations. These dedicated teams can take responsibility of everything from setting cloud budgets and negotiating favorable controls to putting engineering discipline in place to control costs. 3) Automation Even with a dedicated team monitoring cloud use and need, automation is the only way to keep up with the complex and evolving scenarios. Much of today's cloud cost management remains bespoke and manual, In many cases, a monthly report or round-up of cloud waste is the only maintenance done — and highly paid engineers are expected to manually remove abandoned projects and initiatives to free up space. It’s the equivalent of asking someone to delete extra photos from their iPhone each month to free up extra storage. That’s why AI and automation are critical to identify cloud waste and eliminate it. For example: tools like "intelligent auto-stopping" allow users to stop their cloud instances when not in use, much like motion sensors can turn off a light switch at the end of the workday. As cloud management evolves, companies are discovering ways to save millions, if not hundreds of millions — and these 3 principles are key to getting cloud costs under control.

𝗔𝗰𝗵𝗶𝗲𝘃𝗶𝗻𝗴 𝗛𝗶𝗴𝗵 𝗔𝘃𝗮𝗶𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗼𝗻 𝗔𝗪𝗦 𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗕𝗿𝗲𝗮𝗸𝗶𝗻𝗴 𝘁𝗵𝗲 𝗕𝗮𝗻𝗸 99.99% uptime is the goal, but the cost can quickly spiral out of control. • 𝗔𝗪𝗦 𝗥𝗲𝗴𝗶𝗼𝗻𝘀 𝘃𝘀. 𝗔𝘃𝗮𝗶𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗭𝗼𝗻𝗲𝘀: Not every service requires the expense of multi-region failover. Evaluate your RTO/RPO requirements and risk tolerance within the AWS region to determine the appropriate level of redundancy. • 𝗚𝗿𝗮𝗰𝗲𝗳𝘂𝗹 𝗙𝗮𝗶𝗹𝘂𝗿𝗲 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴: Implement circuit breakers and retry mechanisms with AWS services like API Gateway and Step Functions to mitigate transient issues and prevent cascading failures. • 𝗖𝗼𝘀𝘁-𝗘𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲 𝗥𝗲𝗱𝘂𝗻𝗱𝗮𝗻𝗰𝘆: Explore the use of Spot Instances and Reserved Instances (RIs) to achieve redundancy within an AWS region at a fraction of the cost compared to multi-region deployments. • 𝗟𝗲𝘃𝗲𝗿𝗮𝗴𝗲 𝗔𝗪𝗦 𝗦𝗲𝗿𝘃𝗲𝗿𝗹𝗲𝘀𝘀: Consider serverless computing options like AWS Lambda and AWS Fargate, which offer inherent scalability and high availability within an AWS region without extensive infrastructure management. 𝗣𝗿𝗼 𝗧𝗶𝗽: Utilize AWS Route 53 weighted routing to seamlessly balance traffic across your cost-effective failover configurations within an AWS region, ensuring a smooth user experience during disruptions. By carefully considering these strategies, you can build highly available systems on AWS that meet your business needs while optimizing your cloud spending. #AWS #awscommunity

I found companies overpaying $50,000+ on Salesforce Data Cloud simply because they don't understand what truly drives costs. Everyone gets excited about Data Cloud's fancy features but ignores what's actually costing them money. If you don't understand the credit system, you're walking into a financial trap. The truth is simple: every action in Data Cloud costs credits. But some actions are budget killers. What's really emptying your wallet: -It's not just how much data you have—it's what you're doing with it -Sloppy data connections burn through credits like crazy -Poorly designed transformations are silent budget destroyers -Those "simple" activation tasks? They're often credit hogs The formula isn't complicated, just overlooked: (Records processed ÷ 1 Million) × Usage Type = What you're actually paying Smart teams do this first: start with the free version. You get 250,000 credits, one admin, five integration users, and 1TB storage without spending anything. But here's where most fail: they never track which specific operations eat the most credits. Your reports look great while your budget disappears. Want to slash your Data Cloud costs by 50%? Audit which operations are must-haves versus nice-to-haves. Then fix your biggest credit consumers first. Identify your three highest credit-consuming operations and share below. I'll help troubleshoot cost-efficient alternatives.