Tips to Reduce AWS Expenses

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.

𝗛𝗼𝘄 𝗜 𝗦𝗼𝗹𝘃𝗲𝗱 𝗮 𝗖𝗼𝘀𝘁𝗹𝘆 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 𝘄𝗶𝘁𝗵 𝗔𝗺𝗮𝘇𝗼𝗻 𝗦𝗡𝗦 𝗦𝘂𝗯𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝗼𝗻 𝗙𝗶𝗹𝘁𝗲𝗿 𝗣𝗼𝗹𝗶𝗰𝗶𝗲𝘀 ! In a recent project, I encountered a challenge where our system needed to send notifications to multiple teams across various departments. Initially, the solution involved creating separate SNS topics for each team. However, as the number of teams grew, this approach became costly and unmanageable, increasing operational overhead significantly. 𝗧𝗵𝗲 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 How could we optimize this setup to reduce costs and simplify management while ensuring that each team only receives relevant notifications? 𝗧𝗵𝗲 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: 𝗦𝗡𝗦 𝗦𝘂𝗯𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝗼𝗻 𝗙𝗶𝗹𝘁𝗲𝗿 𝗣𝗼𝗹𝗶𝗰𝗶𝗲𝘀 Instead of creating one topic per team, we consolidated everything under a single SNS topic and utilized subscription filter policies to route messages based on attributes dynamically. 𝗛𝗼𝘄 𝗜𝘁 𝗪𝗼𝗿𝗸𝘀 𝗦𝗡𝗦 𝘀𝘂𝗯𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝗼𝗻 𝗳𝗶𝗹𝘁𝗲𝗿 𝗽𝗼𝗹𝗶𝗰𝗶𝗲𝘀 allow you to attach filtering rules to individual subscriptions. Messages published to the topic include attributes such as Department or Priority, and only subscriptions with matching filter policies receive these messages. Now, you can also include a relevant text in the message body as well 𝗙𝗼𝗿 𝗲𝘅𝗮𝗺𝗽𝗹𝗲: We added attributes like Team (e.g., "AWS") to our messages. Each subscription was configured with a filter policy like: 𝗷𝘀𝗼𝗻: {  "𝗧𝗲𝗮𝗺": ["𝗔𝗪𝗦"] } This ensured that only the AWS team received high-priority notifications. 𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀 ✅ 𝗖𝗼𝘀𝘁 𝗦𝗮𝘃𝗶𝗻𝗴𝘀: Consolidating into a single topic reduced the number of SNS topics and associated costs. ✅ 𝗦𝗶𝗺𝗽𝗹𝗶𝗳𝗶𝗲𝗱 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: Managing one topic with well-defined filters eliminated the complexity of juggling multiple topics. ✅ 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Adding new teams or modifying filters became as simple as updating subscription attributes—no need for additional topics. ✅ 𝗣𝗿𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗧𝗮𝗿𝗴𝗲𝘁𝗶𝗻𝗴: Teams received only relevant notifications, reducing noise and improving efficiency. 𝗞𝗲𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀 1️⃣ Use message attributes strategically to define routing logic tailored to your business needs. 2️⃣ Use filter policies for dynamic message delivery instead of duplicating infrastructure. 3️⃣ Monitor and test your policies regularly using AWS tools to ensure they function as expected. This approach not only saved us costs but also made our notification system more robust and scalable. If you're managing notifications for multiple teams or systems, exploring 𝗦𝗡𝗦 𝘀𝘂𝗯𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝗼𝗻 𝗳𝗶𝗹𝘁𝗲𝗿 𝗽𝗼𝗹𝗶𝗰𝗶𝗲𝘀 is highly recommended! Have you used SNS filter policies in your projects? Share your experiences or ask questions in the comments below! 👇 #AWS #SNS #CloudComputing #Serverless #CostOptimization #TechInnovation

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

I finally got our AWS bill under control. Here are three things we did that cut 40% off our monthly spend. 1/ 𝗥𝗲𝗽𝗹𝗮𝗰𝗲𝗱 𝗼𝘂𝗿 𝗯𝗶𝗴 𝗖𝗣𝗨 𝗶𝗻𝘀𝘁𝗮𝗻𝗰𝗲𝘀 𝘄𝗶𝘁𝗵 𝗯𝗮𝗿𝗲-𝗺𝗲𝘁𝗮𝗹 𝗻𝗼𝗱𝗲𝘀 𝗳𝗿𝗼𝗺 𝗢𝗩𝗛 They’re 2-5x cheaper for the same amount of compute. You can avoid paying a one-time setup fee by clicking the "Contact us" button on the OVH website. 2/ 𝗨𝘀𝗲 𝗔𝗿𝗰𝗵𝗲𝗿𝗮 𝗼𝗻 𝘁𝗵𝗲 𝗿𝗲𝗺𝗮𝗶𝗻𝗶𝗻𝗴 𝗔𝗪𝗦 𝗲𝘅𝗽𝗲𝗻𝘀𝗲𝘀 Archera is a middleman between you and AWS that lets you take advantage of long-term reservation discounts on extremely short terms. It lets you get the price of a 3 year reserved instance while only committing to 30 days of usage. 3/ 𝗣𝘂𝘁 𝗮𝗹𝗹 𝗔𝗪𝗦 𝗲𝘅𝗽𝗲𝗻𝘀𝗲𝘀 𝗼𝗻 𝗮𝗻 𝗔𝗺𝗲𝘅 𝗿𝗲𝘄𝗮𝗿𝗱𝘀 𝗰𝗮𝗿𝗱 If your bill is over $5k a month, you’ll get back 1.5x the points. So if you spend 10k a month on AWS, you’ll get 15k airline points, which is a free trip across the country. We travel a lot, and these points save us $1,000s on airline tickets a month. If you want to go even further, the next step is to get off of AWS completely. Move all remaining compute instances to bare-metal providers, like OVH or Hetzner. AWS is the first choice for many companies, but it’s also the most expensive one. Anything else I’m missing?

I recently audited our infrastructure and thought I’d share this. Use AWS Fargate Spot for fault tolerant workloads. I’ve been using it for a few years now, and it has saved up to 70% compared to regular Fargate. Fargate Spot uses AWS’s spare capacity at a lower cost. The trade-off is that AWS can stop your tasks when it needs the capacity back. When this happens, AWS sends a SIGTERM signal, giving your app time to clean up. To make this work, I use checkpoints in batch jobs to save progress periodically. If a task gets interrupted, it resumes right where it left off. For non-urgent workloads, this approach is reliable and saves a lot of money.