Optimize Your AWS Bill with 6 Simple Steps

Nobody ever tells you their infrastructure was badly designed. The AWS bill does. I've reviewed cloud infrastructure for companies spending anywhere from $8k to $800k a month on AWS. The ones bleeding money? Almost never a pricing issue. It is always the same story underneath: An RDS instance running 24/7 for a workload that runs twice a week. Lambda functions hammer a database directly instead of going through a connection pooler. S3 buckets with zero lifecycle policies quietly storing 4 years of logs nobody reads. Nobody made bad decisions. They made fast ones. And fast decisions compound. FinOps teams come in and find Reserved Instance discounts. They right-size a few EC2s. They build dashboards. The bill drops 12%, and leadership celebrates. Then it creeps back up in 8 months. Because the discount didn't fix the Lambda hitting RDS 40,000 times a minute. The right-sizing didn't fix the job that spins up 200 containers to process 14 records. The dashboard doesn't show you the S3 GET requests your application makes on every page load. That's not a cost problem. That's load-bearing inefficiency baked into the design. The honest fix isn't cheaper infrastructure. It's going back to the diagram and asking why traffic flows the way it does. Why does that service call the other service 6 times per request? Optimization is arithmetic. Architecture is the question behind the arithmetic. Fix the question first. I help teams find the architecture problem behind the bill. If that's useful to you, let's talk #AWS #CloudArchitecture #FinOps #CloudCost #SoftwareEngineering

How We Cut an AWS Bill by 52% A client was paying $34K/month on AWS With just 4,000 active users Something was off. So we audited their infrastructure 👇 What we found * Servers running 24/7 (even on weekends) * Database sized for 10x actual usage * No Reserved Instances (all on-demand pricing) * S3 filled with 2 years of unused data * $3K+ monthly data transfer waste * No cost tracking or tagging Not a traffic problem. A decision problem. What we fixed (in 6 weeks) * Switched to Reserved Instances * Right-sized the database * Auto-shutdown for dev & staging * Moved old data to cheaper storage * Reduced unnecessary data transfer * Added proper cost tracking The result * $34,000 → $16,320/month * 52% cost reduction * $212K saved annually * Zero performance impact The truth about cloud costs Most companies aren’t overspending because of usage. They’re overspending because: Infrastructure decisions are never revisited. Simple rule If your AWS bill is growing faster than your users… You don’t have a scaling problem. You have an architecture problem. At Crescent Webs, we help startups optimize cloud infrastructure, reduce costs by 30–60%, and build scalable DevOps systems without affecting performance. 📩 info@crescentwebs.com 🌐 www.crescentwebs.com Comment CLOUD if you want a free audit of your AWS setup. #DevOps #AWS #CloudCostOptimization #CrescentWebs #CloudComputing #StartupGrowth #TechLeadership

##I_cut_my_company’s_AWS_bill_by_30%_in_just_3 weeks. No hacks. No risky changes. Just fixing 4 expensive mistakes most teams ignore. If you're running workloads on AWS, read this carefully 👇 💸 1. EC2 Right-Sizing (Biggest Hidden Waste): Most teams over-provision “just in case.” → Found instances running at <20% CPU → Downsized m5.xlarge → m5.large (6 instances) 💰 Saved: $420/month 📉 2. Reserved Instances + Savings Plans: On-demand = convenience tax. → Locked baseline with 1-year Reserved Instances → Layered Savings Plans for flexibility 💰 Result: 42% drop in EC2 cost 📦 3. S3 Lifecycle Optimization: Old logs sitting in Standard = silent money leak. → 30 days → IA → 90 days → Glacier 💰 Cost: $380 → $94/month 🌐 4. NAT Gateway Trap (Most Overlooked): NAT Gateway charges per GB processed. → Replaced with VPC Endpoints (S3, DynamoDB) 💰 Saved: $200+/month 🚀 Final Impact: ✔ 30% total cost reduction ✔ $1,800/month saved ✔ $21,600/year back to business ✔ Zero downtime Cloud cost optimization is not optional anymore. It’s a core DevOps skill in 2025. If you're working on AWS and haven’t done a cost audit yet — start here. ♻️ Repost if this helps your team 👇 Drop your best cost-saving trick #AWS #DevOps #CloudCost #FinOps #CloudEngineering #AWSArchitecture

Elastic Beanstalk looks simple on the bill until you pull it apart. Most teams know they pay for the EC2 instances, which start at $0.0042 per hour for t4g.nano in us-east-1. However, additional costs can accrue from associated resources such as Application Load Balancers, charged at $0.008 per LCU-hour on top of the hourly ALB fee. One often overlooked aspect is the data transfer charges, which can quickly add up, especially if your application handles substantial traffic. AWS provides 100 GB of free data transfer out per month aggregated across all services, but beyond that you pay $0.09 per GB — a cost that can surprise teams who don't account for it in their budgeting. Furthermore, there are potential charges for storing application versions and logs in S3, typically around $0.023 per GB for S3 Standard. These small but frequent expenses can contribute significantly to your overall bill if not monitored regularly. A key takeaway is to regularly review your configurations and usage to identify idle resources that may incur costs without providing value. Implementing periodic cost hygiene reviews is essential for managing your Elastic Beanstalk expenses effectively. #AWS #CloudCost #ElasticBeanstalk #FinOps #CostOptimization #DevOps #CloudComputing #AWSBilling

AWS Doesn’t Fail — Your Architecture Does After working on multiple production systems, I’ve noticed a common reaction during outages: 👉 “AWS is down” But in most cases… it isn’t. --- 🔴 Real Problem: A client-facing system went down during peak traffic. Initial assumption: “Something is wrong with AWS” Actual cause: - Single EC2 instance handling all traffic - No load balancing - Database running on a single point of failure - No auto-scaling configured 👉 Result: - Complete downtime - Lost user trust - Revenue impact --- 🟢 Reality Check: Cloud platforms like AWS are highly reliable. What usually fails is: - Poor architecture decisions - Lack of redundancy - No traffic handling strategy --- 🟢 What Fixed It (Production-Ready Setup): ✔️ Load Balancing Distributed traffic across multiple instances → No single server overload ✔️ Auto-Scaling Scaled infrastructure based on traffic → Handled peak load automatically ✔️ Failover Database Setup Primary + replica configuration → System stayed live even during DB issues ✔️ Health Checks & Monitoring → Issues detected before users noticed 💡 What Changed: - Zero downtime during high traffic - System became fault-tolerant - Better performance under load --- 💡 Lesson: Cloud is reliable. But reliability is your responsibility. --- If your system goes down, don’t blame the cloud first. Check your architecture. #AWS #Cloud #SystemDesign #Backend #DevOps #Scalability

Uploading large files to S3 from another region? Here’s how I fixed slow transfers If you’ve ever uploaded large files to S3 from a distant location, you’ve probably noticed: Uploads are slower than expected Latency becomes a bottleneck I recently worked on a use case where data had to be uploaded from a different region, and standard S3 uploads were taking too long. That’s where S3 Transfer Acceleration helped. Using Amazon S3 Transfer Acceleration, uploads are routed through AWS edge locations (same backbone used by CloudFront), which significantly improves speed over long distances. How it works Instead of uploading directly to S3: #https://yourbucketendpoint You use: #https://*.https://lnkd.in/gPKXK8uj 👉 Data enters the nearest AWS edge location 👉 Travels through AWS global network 👉 Reaches S3 faster and more reliably When to use it Uploading data from different geographic regions Large file transfers (GBs/TBs) Global applications with distributed users Real-World Consideration It’s not always faster for same-region uploads Comes with additional cost Should be tested before enabling in production Quick Enable Steps Enable Transfer Acceleration on your S3 bucket Update endpoint to https://*.https://lnkd.in/gPKXK8uj Test performance Why these matters In one scenario, we reduced upload time significantly just by switching endpoints, no major architecture change needed. Sometimes, small AWS features make a big difference. #AWS #Cloud #DevOps #S3 #AWSTips #CloudEngineer

Ever wondered what building a tech startup really looks like behind the scenes? We’re opening up our roadmap to the world. Instead of guessing what to build next, we’re doing something simple: asking you what you’d actually pay for. If you had to choose,which one of these intrigues you the most: - DB Cron: run recurring DB jobs without external cron - Auto Indexing: detect and fix missing indexes automatically - Cloud Real-Time Monitoring: live dashboards + alerts - SkyScanner-Style DB Pricing: compare costs across AWS regions - DB Storage Downsizing: reclaim unused storage automatically We’re building this with you, not for you. Take a look → https://lnkd.in/gyMmD4Hp #postgresql #database #managedDatabases #devops #aws #buildinpublic

All the best to Selfhost team. I feel Database storage downsizing will be the best idea or feature that would be enabled by Selfhost followed by ideas managed database auto scheduler feature and Auto indexing feature with much detailed and finer control.

I ran a full infrastructure audit that cut an AWS bill by 33%, saving $1,500 per month with zero impact on reliability. Standard AWS deployments often suffer from cost creep because provisioning drifts past its original justification. When version pins aren't reviewed and storage tiers are chosen too conservatively, you end up paying a premium for resources that don't drive performance. The optimization targeted six specific areas across the stack: • 𝗖𝗼𝗺𝗽𝘂𝘁𝗲: Terminated idle EC2 instances and migrated stateless services to EKS Spot instances. • 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲: Upgraded RDS PostgreSQL to exit expensive extended support windows and migrated storage from provisioned IOPS (io1) to gp3. • 𝗖𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗿𝘀: Upgraded EKS clusters to standard support versions to eliminate vCPU/hour surcharges. • 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝗶𝗻𝗴: Consolidated multiple ALBs into a single load balancer using path-based and host-based routing. By moving away from "conservative" defaults, we changed the cost profile of the environment: • 𝗩𝗲𝗿𝘀𝗶𝗼𝗻 𝗦𝘂𝗽𝗽𝗼𝗿𝘁: Upgrading EKS and RDS removed persistent surcharges for out-of-support versions. • 𝗦𝗽𝗼𝘁 𝗨𝘁𝗶𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Using Karpenter to provision Spot nodes with On-Demand fallback reduced compute costs by ~60%. • 𝗦𝘁𝗼𝗿𝗮𝗴𝗲 𝗧𝗶𝗲𝗿𝘀: gp3 provided the required 3,000 IOPS without the separate provisioning charges of io1. Outcome: • Monthly bill reduced from $4,500 to $3,000. • $18,000 in annual recurring savings. • Cleaned up multi-region resource sprawl and redundant load balancer fixed costs. The production lesson:  Cloud waste isn't usually one massive error; it’s the accumulation of "extended support" premiums and idle resources across multiple regions. If you haven't reviewed your version pins and storage tiers in six months, you are overpaying. If you're running a non-trivial AWS footprint, this is worth looking at. DMs open or reach me via jakops.cloud Read the full breakdown here: https://lnkd.in/dyxXPKKa #AWS #CostOptimization #CloudEngineering #DevOps

Going from one agent to hundreds across an enterprise gets messy fast. Which agents already exist? Who's allowed to publish what? How many teams are quietly rebuilding the same thing? We've been working on this problem with AWS Agent Registry in AgentCore — and it's now in preview. One of the top asks from our GFS customers. It's a single place to catalog, discover, and reuse agents, tools, MCP servers, and agent skills regardless of where they're built. On AWS, other clouds, on-prem. It indexes all of it. The search is hybrid — keyword plus semantic — so "payment processing" surfaces tools tagged "billing" or "invoicing" even if they're named differently. There's a built-in approval workflow that takes records from draft to pending to discoverable, with full lifecycle tracking through retirement. And it's accessible as an MCP server, so tools like Kiro and Claude Code can query it directly from the IDE. Blog: https://lnkd.in/eWRVNQEp