AWS Bill Reduced by 37% with Kubernetes and Cloud Optimization

🚀 What is Load Balancer in AWS? After learning Auto Scaling… Let’s understand how traffic is distributed 👇 ⚖️ What is a Load Balancer? A Load Balancer distributes incoming traffic across multiple EC2 instances. 👉 Simple idea: Load Balancer = Traffic distributor 🔹 Why is it important? ⚡ Prevents overload on one server 🔁 Improves availability 📈 Works with Auto Scaling 🔹 How it works: 1️⃣ User sends request 2️⃣ Load Balancer receives traffic 3️⃣ Distributes to multiple servers 🔹 Real-life example: Like a traffic police officer who directs vehicles to different roads 💡 Real Insight: Without Load Balancer… one server may crash under heavy traffic #AWS #LoadBalancer #DevOps #CloudComputing #LearningInPublic

🚀 From EC2 to Production-Ready Load Balancer Setup ☁️ Just completed setting up a production-level load balancing architecture on AWS — here’s a structured breakdown of the journey 👇 🔹 1. Security First 🔐 Configured a Security Group with controlled inbound access: ✅ Port 22 – SSH (for admin access) 🌐 Port 80 – HTTP 🔒 Port 443 – HTTPS 👉 Ensuring only required traffic is allowed is the first step toward a secure infra 🔹 2. Scalable Compute Layer ⚙️ 🚀 Launched 3 EC2 instances 🧩 Attached same Security Group for consistency 📜 Used User Data scripts to automate setup 👉 Result: Identical, ready-to-serve application servers 🔹 3. Target Group Setup 🎯 🔗 Created a Target Group (TG) 💓 Configured health checks for reliability 🔄 Registered EC2 instances to TG 👉 Ensures traffic goes only to healthy instances 🔹 4. Load Balancer with High Availability 🌍 ⚖️ Created Application Load Balancer (ALB) 🌐 Enabled Multi-AZ deployment 🔁 Distributes traffic evenly across instances 💥 Provides fault tolerance & zero single point of failure 📸 Live Proof: EC2 instance up & running in ap-south-1a 💪 💡 Key Takeaway: A production-ready setup isn’t just about launching servers — it’s about security, automation, scalability, and resilience working together seamlessly. Brijesh Bapat Rutuja Tandel Vaibhav Kokare Ulhas Narwade (Cloud Messenger☁️📨) Amazon Web Services (AWS) 🔥 Next Step: Add Auto Scaling + Monitoring for a fully automated infra! #AWS #CloudComputing #DevOps #LoadBalancer #EC2 #SystemDesign #CloudArchitecture

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

𝗔𝗪𝗦 𝗟𝗮𝗺𝗯𝗱𝗮 𝘁𝘂𝗿𝗻𝗲𝗱 𝘀𝗲𝗿𝘃𝗲𝗿𝘀 𝗶𝗻𝘁𝗼 𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 At Amazon Web Services (AWS), infrastructure doesn’t always mean servers. Sometimes, it’s just code that runs when needed. That changes how applications are built. Without serverless: • teams manage idle infrastructure • scaling requires planning • costs grow with unused resources With AWS Lambda, teams run 𝗲𝘃𝗲𝗻𝘁-𝗱𝗿𝗶𝘃𝗲𝗻 𝗰𝗼𝗱𝗲 𝘁𝗵𝗮𝘁 𝘀𝗰𝗮𝗹𝗲𝘀 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗰𝗮𝗹𝗹𝘆. The DevOps lesson: 𝗗𝗼𝗻’𝘁 𝗺𝗮𝗻𝗮𝗴𝗲 𝘀𝗲𝗿𝘃𝗲𝗿𝘀. 𝗠𝗮𝗻𝗮𝗴𝗲 𝗲𝘃𝗲𝗻𝘁𝘀. When compute becomes on-demand, you only pay for what you use. At ServerScribe, we help teams design architectures that scale automatically — without operational overhead. Are you still managing servers — or building serverless systems? 👇 #DevOps #ServerScribe #AWSLambda #Serverless #CloudComputing #SRE #Scalability

Many teams building Amazon Web Services (AWS) containerized workloads often face the same friction: managing infrastructure complexity without overengineering. This MechCloud template shows how to run a production-ready ECS Fargate service without touching servers or clusters. Here’s what this template sets up: - ECS Fargate service for serverless container execution   - Task definitions with CPU and memory configuration   - Application Load Balancer for traffic distribution   - Networking with VPC, subnets and security groups   - IAM roles for secure service execution   - Auto scaling configuration for demand-based scaling  Why this matters: - No EC2 management overhead   - Clean separation between infra and application   - Predictable scaling behavior   - Secure by default setup   - Faster path from idea to deployment  If you're building microservices or APIs on AWS, this is a solid baseline to start with and extend. Explore the template:   https://lnkd.in/g3j3PCk5 #DevOps #AWS #SRE #PlatformEngineering #InfrastructureAsCode

💡 AWS Tip of the Day – EFS Performance Optimization When using Amazon Elastic File System, choose the right performance mode based on your workload: * General Purpose Mode 👉 Best for low-latency use cases like web servers, CMS, and DevOps pipelines * Max I/O Mode 👉 Suitable for high-throughput workloads like big data, analytics, and parallel processing 👉 Also, enable EFS Lifecycle Management to automatically move infrequently accessed files to a cheaper storage class (EFS IA) and reduce costs. 🚀 Pro Tip: Mount EFS across multiple EC2 instances in different AZs for high availability + shared storage, perfect for container workloads (ECS/EKS). #AWS #EFS #CloudComputing #DevOps #AWSTips #CloudOptimization #Infrastructure #SRE #TechTips #AmazonWebServices

Day 2 – AWS Compute: When NOT to use what? Yesterday, we explored when to use compute services. Today, we shift our focus to the other side of the coin. Knowing when NOT to use a service is crucial for any architect. ⚙️ EC2 – When NOT to use: ❌ For short-lived or event-driven workloads ❌ When you don’t want to manage servers ❌ For unpredictable traffic (scaling delay) 👉 Why: Requires provisioning, patching, and scaling management ⚡ Lambda – When NOT to use: ❌ Long-running tasks (>15 minutes) ❌ Heavy CPU/memory workloads ❌ Applications needing persistent connections 👉 Why: Execution limits, cold starts, and stateless nature 🐳 ECS – When NOT to use: ❌ If you need Kubernetes standard ❌ Very small/simple apps (overkill) ❌ If you want zero infrastructure management (use Fargate instead) 👉 Why: Still requires cluster and scaling decisions ☸️ EKS – When NOT to use: ❌ Small teams or beginners ❌ Simple applications ❌ When Kubernetes expertise is missing 👉 Why: High complexity and operational overhead 🚀 Fargate – When NOT to use: ❌ Need deep control over infrastructure ❌ Cost-sensitive long-running workloads ❌ Specialized compute requirements (GPU, custom OS tuning) 👉 Why: Higher cost compared to EC2 and less control 🌱 Elastic Beanstalk – When NOT to use: ❌ Complex microservices architecture ❌ Need full infrastructure customization ❌ Advanced DevOps pipelines 👉 Why: Abstracts infrastructure but limits flexibility 📦 AWS Batch – When NOT to use: ❌ Real-time processing systems ❌ Low-latency APIs ❌ Event-driven microservices 👉 Why: Designed for batch jobs, not real-time ⚖️ Architect Mindset: If it’s simple → avoid complex services (EKS, ECS) If it’s event-driven → avoid EC2 If it’s long-running → avoid Lambda If it’s cost-sensitive → evaluate Fargate carefully 🧠 Golden Rule 👉 “Just because you CAN use a service doesn’t mean you SHOULD.” More coming tomorrow 🔥 Next: Storage – When to use S3, EBS, EFS 👉 I’m planning to deep dive into each of these services with real-world architectures. 💬 Which AWS Compute service do you want me to cover in detail next? Drop it in the comments 👇 EC2 Lambda ECS EKS Fargate #AWS #CloudComputing #SolutionArchitect #SystemDesign #DevOps #LearningInPublic

👉 𝗦𝗲𝗿𝘃𝗲𝗿𝗹𝗲𝘀𝘀 (𝗟𝗮𝗺𝗯𝗱𝗮) 𝘃𝘀 𝗘𝗖𝟮 Both can run your code — but the approach is completely different 💡 𝗦𝗶𝗺𝗽𝗹𝗲 𝗮𝗻𝗮𝗹𝗼𝗴𝘆: • Lambda → Order food (no setup, no maintenance) • EC2 → Cook yourself (full control, more effort) 🎯 𝗞𝗲𝘆 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲𝘀: 𝗟𝗮𝗺𝗯𝗱𝗮: • No server management • Auto scales • Pay per execution 𝗘𝗖𝟮: • Full control over system • Runs continuously • More responsibility 🧠 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁 𝗺𝗶𝗻𝗱𝘀𝗲𝘁: • Don’t default to EC2 • First ask → Can this be serverless? • Use Lambda for event-driven workloads • Use EC2 for long-running or complex systems 🔧 Real-world example: 𝗨𝗽𝗹𝗼𝗮𝗱𝗶𝗻𝗴 𝗮 𝗳𝗶𝗹𝗲 𝘁𝗼 𝗦𝟯 𝗰𝗮𝗻 𝘁𝗿𝗶𝗴𝗴𝗲𝗿 𝗮 𝗟𝗮𝗺𝗯𝗱𝗮 𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻 𝘁𝗼 𝗽𝗿𝗼𝗰𝗲𝘀𝘀 𝗶𝘁 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗰𝗮𝗹𝗹𝘆 📌 𝙏𝙝𝙚 𝙗𝙚𝙨𝙩 𝙖𝙧𝙘𝙝𝙞𝙩𝙚𝙘𝙩𝙪𝙧𝙚 𝙤𝙛𝙩𝙚𝙣 𝙪𝙨𝙚𝙨 𝙇𝙀𝙎𝙎 𝙨𝙚𝙧𝙫𝙚𝙧𝙨, 𝙣𝙤𝙩 𝙢𝙤𝙧𝙚 #AWS #Lambda #Serverless #CloudArchitecture #DevOps #SolutionsArchitect #LearningInPublic

Website link: www.systemdrd.com Managing servers is slowing your team down. Even for simple tasks, you still deal with: Infrastructure setup Patching and maintenance Scaling concerns 💥 This operational overhead takes focus away from building real features. 💡 Enter Serverless Computing. With services like AWS Lambda: ➡️ No server management ➡️ Automatic scaling ➡️ Pay only for what you use ⚡ This means: ✔ Faster development ✔ Lower costs ✔ Better scalability ✔ Focus on product, not infrastructure 📌 Serverless isn’t about “no servers” — it’s about not managing them. 💭 Curious — are you using serverless in your projects yet? #Serverless #CloudComputing #DevOps #BackendEngineering #SystemDesign #Microservices #Scalability #SoftwareArchitecture #TechLeadership #AWS

Running workloads on EKS and still getting a huge AWS bill? Here are 9 ways to fix it - and WHY each one works: 1️⃣ Spot Instances for stateless workloads Stateless pods can be interrupted safely. Use unused EC2 capacity at up to 90% off. 2️⃣ Deploy Karpenter Provisions the exact node your pod needs. Terminates idle nodes automatically. 3️⃣ Compute Savings Plans Predictable workload? A 1-year plan saves ~40% with zero upfront cost. 4️⃣ Set CPU & memory limits on every pod Without limits, one greedy pod wastes an entire node you're paying for. 5️⃣ Scale down dev/staging after hours Non-prod doesn't need to run 24/7. A simple CronJob can cut dev costs by 65%. 6️⃣ Minimise inter-AZ traffic AWS charges per byte across AZs. Topology spread constraints keep chatty pods together. 7️⃣ Enable Kubecost or OpenCost You can't optimise what you can't see. Get cost visibility per namespace and team. 8️⃣ Delete unused EBS volumes Deleted pods leave orphaned volumes.Audit and delete unused volumes.Set AWS DLM policies to auto-clean snapshots. 9️⃣ Enforce Namespace ResourceQuota One misconfigured deployment can spike your bill. Hard caps per namespace prevent that. Apply even 3 of these and your next AWS bill will look different. Which one are you NOT doing yet? 👇 #AWS #EKS #Kubernetes #FinOps #CloudCost #DevOps #K8s #SolutionsArchitect