Cost Optimization in DevOps

I reduced our Annual AWS bill from ₹15 Lakhs to ₹4 Lakhs — in just 6 months. Back in October 2024, I joined the company with zero prior industry experience in DevOps or Cloud. The previous engineer had 7+ years under their belt. Just two weeks in, I became solely responsible for our entire AWS infrastructure. Fast forward to May 2025, and here’s what changed: ✅ ECS costs down from $617 to $217/month — 🔻64.8% ✅ RDS costs down from $240 to $43/month — 🔻82.1% ✅ EC2 costs down from $182 to $78/month — 🔻57.1% ✅ VPC costs down from $121 to $24/month — 🔻80.2% 💰 Total annual savings: ₹10+ Lakhs If you’re working in a startup (or honestly, any company) that’s using AWS without tight cost controls, there’s a high chance you’re leaving thousands of dollars on the table. I broke everything down in this article — how I ran load tests, migrated databases, re-architected the VPC, cleaned up zombie infrastructure, and built a culture of cost-awareness. 🔗 Read the full article here: https://lnkd.in/g99gnPG6 Feel free to reach out if you want to chat about AWS, DevOps, or cost optimization strategies! #AWS #DevOps #CloudComputing #CostOptimization #Startups

🧾 Today I automated a full AWS cost-saving audit using nothing but Bash, AWS CLI, and jq. ✅ To learn more, checkout the project: https://lnkd.in/efmb-uBw As a DevOps engineer, I’ve seen how cloud costs can sneak up when environments grow - especially in multi-team setups. So I built a suite of scripts to scan for common silent budget killers: 🔍 What the audit covers: 💸 On-Demand EC2 Instances - not covered by Savings Plans or Reserved Instances 🧹 Unattached (forgotten) EBS volumes - still billing after EC2 is gone 🗓️ Old RDS snapshots - sitting idle and growing in size 🗃️ S3 buckets without lifecycle policies - no object expiration = endless cost 🌐 Data transfer risks - public IPs, missing VPC endpoints, cross-AZ traffic 🛑 Idle Load Balancers - ALBs/NLBs with 0 traffic in days = money drain Each script logs results with summaries, and suggestions. The best part? No third-party tools. Just raw AWS CLI power and CloudWatch metrics. ✅ If you're managing cloud infrastructure, it's worth automating cost hygiene like this. Want to exchange ideas or set this up in your environment? Let’s connect. #aws #devops #finops #cost #optimization #bash

Saving Lakhs Every Month - How I Implemented an AWS Cost Optimization Automation as a DevOps Engineer! When I first joined my current project as an AWS DevOps Engineer, one thing immediately caught my attention: “Our AWS bill was silently bleeding every single day.” Thousands of EC2 instances, unused EBS volumes, idle RDS instances, and most importantly — NO real-time cost monitoring! Nobody had time to manually monitor resources. Nobody had visibility on what was running unnecessarily. Result? Month after month, the bill kept inflating like a balloon. ⸻ I decided to take this as a personal challenge. Instead of another boring “cost optimization checklist,” I built a fully automated cost-saving architecture powered by real-time DevOps + AWS services. Here’s exactly what I implemented: ⸻ The Game-Changing Solution: 1. AWS Config + EventBridge: • I set up Config rules to detect non-compliant resources — like untagged EC2, open ports, idle machines. 2. Lambda Auto-Actions: • Whenever Config detected issues, EventBridge triggered a Lambda function. • This function either auto-tagged, auto-stopped idle instances, or sent immediate alerts. 3. Scheduled Cost Anomaly Detection: • Every night, a Lambda function pulled daily AWS Cost Explorer data. • If any service or account exceeded 10% threshold compared to the weekly average, it triggered Slack + Email alerts. 4. Visibility First, Action Next: • All alerts first came to Slack channels where DevOps and owners could approve actions (like terminating unused resources). 5. Terraform IaC: • Entire solution — Config, EventBridge, Lambda, IAM, SNS — all written in Terraform to ensure version control and easy replication. ⸻ The Impact: • 20% monthly AWS cost reduction within the first 2 months. • Real-time visibility for DevOps and CloudOps teams. • Zero human dependency for basic compliance enforcement. • First-time ever — proactive action before bills got out of hand! ⸻ Key Learning: “Real success in DevOps isn’t just about automation — it’s about understanding business pain points and solving them smartly.” I learned that cost optimization is NOT a “one-time” audit. It needs real-time event-driven systems — combining AWS Config, EventBridge, Lambda, Cost Explorer, and Slack. ⸻ If you’re preparing for DevOps + AWS roles today: Don’t just learn services individually. Learn how to build real-world solutions. Show how you saved time, money, and risk — that’s what companies pay for! ⸻ If you want me to share the full Terraform + Lambda GitHub repo for this cost optimization automation project, Comment below: “COST SAVER” and I will send you the link! Let’s learn. Let’s grow. Let’s solve REAL problems! #DevOps #AWS #CostOptimization #RealTimeAutomation #CloudComputing #LearningByDoing

The Data engineering things Databricks Cost Reduction! Interviewers: Can you share some advanced strategies you’ve used to reduce costs, with examples and figures?" Candidate: strategies for cost optimization. Advanced Strategies Optimizing Job Scheduling and Cluster Management: Interviewer: "How do you handle job scheduling to optimize costs?" Candidate: "I implemented a strategy where we grouped jobs with similar resource requirements and execution times to run sequentially on the same cluster, reducing the number of cluster spin-ups and terminations." Figures: Before : Clusters were started for each job, leading to frequent initialization costs. Monthly cost was around $8,000. After : By grouping jobs, we reduced the cluster initialization instances by 50%, bringing the cost down to $5,000. Savings: $3,000 per month, a 37.5% reduction. Dynamic Resource Allocation Based on Workload Patterns: Interviewer: "Can you explain how dynamic resource allocation works in your setup?" Candidate: "We analyzed workload patterns to predict peak usage times and adjusted cluster sizes dynamically. For example, during non-peak hours, we reduced the cluster size significantly." Figures: Before : Clusters were over-provisioned during non-peak hours, costing about $10,000 monthly. After : Adjusting cluster size dynamically during off-peak hours saved us $4,000 monthly. Savings: $4,000 per month, a 40% reduction. Using Job Execution Notebooks Efficiently: Interviewer: "How do you optimize notebook execution to save costs?" "We identified and modularized our notebooks to avoid unnecessary execution. By running only the essential parts of the notebook and reusing cached results, we significantly reduced computation time and resource usage." Figures: Before : Full notebook execution for each job cycle cost $7,000 monthly. After : $4,500 monthly. Savings: $2,500 per month, a 35.7% reduction. Interviewer: "Can you provide a specific tricky scenario where you optimized costs unexpectedly?" Candidate: "Certainly. In one project, we realized that our data ingestion process was the costliest component due to high data volumes and frequent updates." Problem: High Ingestion Costs: Candidate: "The ingestion process was initially costing us around $12,000 per month." Solution: Incremental Data Processing: Candidate: "We shifted to an incremental data processing approach using Delta Lake. Instead of processing entire datasets, we processed only the changes." Figures: Before: Full dataset processing cost $12,000 monthly. After : Incremental processing reduced the costs to $6,000 monthly. Savings: $6,000 per month, a 50% reduction. Unexpected Benefit: Reduced Data Storage Costs: Candidate: "As a side benefit, our storage costs also dropped because we were storing fewer interim datasets." Figures: Storage Costs Before: $3,000 monthly. Storage Costs After: $1,800 monthly. Savings: $1,200 per month, a 40% reduction.

Just slashed a client's dev environment costs by 64% using AWS CDK and EventBridge Scheduler. The solution? 50 lines of core logic, zero maintenance overhead. Here's the breakdown: Their dev environment was running 24/7 – a common oversight I see in many AWS setups. Multiple RDS instances and EC2 servers were consuming resources during off-hours, essentially burning money while developers sleep. The solution leverages AWS EventBridge Scheduler with AWS CDK for infrastructure as code: - Automated start/stop schedules for RDS and EC2 instances (weekdays 7 AM - 7 PM) - IAM roles and permissions handled through CDK constructs - Dead Letter Queue for failed operations monitoring - Timezone-aware scheduling (critical for distributed teams) - Zero manual intervention needed after deployment The real power isn't just in the cost savings – it's in the maintainability. One CDK construct can manage multiple instances, and adding new resources is as simple as updating an array of identifiers. Key metrics: - 108 hours/week reduction in runtime - 64% reduction in dev environment costs - Resource utilization aligned with actual working hours - 10-minute deployment time - ROI from day one Are you still running your dev instances 24/7? #AWS #CloudCost #IaC #DevOps #AWSCDK #CostOptimization

🚀 𝐇𝐨𝐰 𝐖𝐞 𝐂𝐮𝐭 𝐊𝐮𝐛𝐞𝐫𝐧𝐞𝐭𝐞𝐬 𝐂𝐨𝐬𝐭𝐬 𝐛𝐲 60% 𝐖𝐢𝐭𝐡𝐨𝐮𝐭 𝐃𝐨𝐰𝐧𝐭𝐢𝐦𝐞 Cloud costs were skyrocketing, and after a deep dive, I found hidden inefficiencies bleeding our budget. 🔥 𝐓𝐨𝐩 𝐊𝐮𝐛𝐞𝐫𝐧𝐞𝐭𝐞𝐬 𝐂𝐨𝐬𝐭 𝐂𝐮𝐥𝐩𝐫𝐢𝐭𝐬 𝐖𝐞 𝐅𝐨𝐮𝐧𝐝: ✅ Idle workloads running 24/7—even when no one was using them. ✅ Over-provisioned CPU & memory, wasting compute power. ✅ Unoptimized autoscaling, keeping expensive nodes active. ✅ Orphaned resources—Persistent Volumes, Load Balancers, and Zombie Pods. ✅ Mismanaged Spot Instances, leading to unexpected evictions & higher on-demand costs. ✅ Excessive network egress charges, especially from cross-region traffic. 🔍 𝐇𝐞𝐫𝐞’𝐬 𝐇𝐨𝐰 𝐖𝐞 𝐅𝐢𝐱𝐞𝐝 𝐈𝐭 & 𝐒𝐥𝐚𝐬𝐡𝐞𝐝 𝐂𝐨𝐬𝐭𝐬 𝐛𝐲 60% 1️⃣ 𝑺𝒎𝒂𝒓𝒕𝒆𝒓 𝑨𝒖𝒕𝒐𝒔𝒄𝒂𝒍𝒊𝒏𝒈: 𝑲𝒂𝒓𝒑𝒆𝒏𝒕𝒆𝒓 + 𝑽𝑷𝑨 + 𝑯𝑷𝑨 ✅ Replaced Cluster Autoscaler with Karpenter for faster & cost-aware node provisioning. ✅ Used Vertical Pod Autoscaler (VPA) to automatically adjust CPU/memory requests. ✅ Optimized Horizontal Pod Autoscaler (HPA) to scale pods dynamically based on actual traffic patterns. 2️⃣ 𝑺𝒄𝒉𝒆𝒅𝒖𝒍𝒆𝒅 & 𝑶𝒏-𝑫𝒆𝒎𝒂𝒏𝒅 𝑾𝒐𝒓𝒌𝒍𝒐𝒂𝒅𝒔 𝒘𝒊𝒕𝒉 𝑲𝑬𝑫𝑨 & 𝑨𝒓𝒈𝒐 𝑾𝒐𝒓𝒌𝒇𝒍𝒐𝒘𝒔 ✅ Used KEDA to spin up workloads only when needed—no more idle background jobs. ✅ Moved non-critical workloads to Argo Workflows, reducing long-running container costs. ✅ Paused dev/test clusters automatically after work hours using custom automation. 3️⃣ Cleaning Up Wasted Resources (Automated) ✅ Ran kubectl top & Kubecost to find & kill over-provisioned workloads. ✅ Created a Garbage Collector Controller to detect & delete: 🔹 Orphaned PVs & PVCs (saved ~$2,000/month). 🔹 Unused Load Balancers & Ingresses. 🔹 Zombie Services & stale Helm releases. 4️⃣ Network Cost Optimization: Egress & Load Balancers ✅ Reduced cross-region traffic by keeping microservices in the same availability zone. ✅ Used Cilium for service-to-service communication, avoiding unnecessary egress charges. ✅ Optimized Load Balancers with Ingress NGINX & Internal Load Balancers to cut external traffic costs. 5️⃣ Smarter Spot Instance Management with Karpenter & Ocean by Spot ✅ Used Karpenter to prioritize Spot Instances while ensuring fallback to On-Demand only when needed. ✅ Implemented Spot.io Ocean to dynamically move workloads across instance types for better cost efficiency. 🔥 The Impact ✅ Cloud spend dropped from $15,000 → $6,000 per month ✅ Zero downtime for production workloads ✅ Automated alerts for cost anomalies & resource spikes 💡 Pro Tip: Don’t Just Look at Nodes! 🔹 Check for unused Persistent Volumes & Load Balancers 🔹 Optimize network traffic to reduce egress costs 🔹 Automate workload shutdowns when idle 💬 Want access to the YAMLs & automation scripts we used? Drop a comment, and I’ll share the GitHub repo! #Kubernetes #CloudCostOptimization #DevOps #FinOps #K8s #CloudComputing #SRE #Observability #CostReduction #KEDA #Karpenter #Kubecost

The Costly Mistake in AI Projects: Ignoring DevOps Until Deployment One of the biggest financial drains in AI projects? Companies focusing on DevOps only when they deploy, while ignoring it during development and experimentation. The result? Wasted compute, skyrocketing cloud bills, and inefficiencies that bleed resources. 🔥 Where AI Teams Waste Money Without Realizing It 🚨 Over-Provisioning Compute: Data scientists spin up massive GPU instances for experimentation but forget to shut them down. Some jobs could run on CPUs instead, saving thousands. 🚨 Inefficient Model Training: Retraining full models instead of leveraging incremental learning or caching intermediate steps. 🚨 No Monitoring for Cloud Costs: AI teams often treat cloud expenses as an afterthought—until they get hit with shocking invoices. 🚨 Storage Sprawl: Duplicated datasets, unoptimized data pipelines, and unused model checkpoints piling up. 🚨 Expensive Inference & Serving: Running AI models on overpowered, always-on VMs when serverless or edge computing could drastically cut costs. ⸻ 💡 Best Practices: Reducing AI Costs with Smart DevOps ✅ Implement DevOps from Day 1 – Not just at deployment. Automate infrastructure scaling, data pipeline optimizations, and model versioning during development. ✅ Use Auto-Scaling & Spot Instances – Ensure training and inference workloads scale up only when needed and take advantage of cheaper spot/reserved instances. ✅ Monitor & Set Budgets – Implement FinOps principles: track AI spend in real-time, set up auto-alerts, and optimize underutilized resources. ✅ Optimize Model Training – Use techniques like transfer learning, quantization, and model pruning to reduce compute costs without sacrificing accuracy. ✅ Containerize Everything – Running models in Docker & Kubernetes ensures efficient resource usage and avoids over-provisioning. ✅ Choose the Right Deployment Strategy – For low-latency applications, use edge computing. For variable workloads, go serverless instead of dedicated VMs. ⸻ 💰 The Bottom Line AI is expensive—but reckless DevOps strategies make it even costlier. The companies that integrate DevOps early (not just at deployment) slash costs, improve efficiency, and scale sustainably. 🚀 Is your AI team proactive about DevOps, or do they wait until it’s too late? Let’s discuss in the comments! 👇 #AI #DevOps #FinOps #MachineLearning #CloudComputing #MLOps

Your AWS bill isn’t just about infrastructure. It’s about how your team uses it. We audited an AWS account spending $45,000/month. Guess what? Over $18,000 was just bad DevOps habits. Here’s what we found (and how to fix it): 1️⃣ Overprovisioned everything → EC2s 5x larger than needed → RDS clusters at <10% usage → Lambda functions maxed out by default Set-and-forget costs money. 2️⃣ No tagging = chaos → Idle EBS volumes, zombie load balancers → No clue who owns what → No one wants to delete “just in case” Tag by team, project, and environment. Always. 3️⃣ Manual deployments = money leaks → Full env spin-ups for rollbacks → Old versions still running → No CI/CD = more human errors Automation isn’t optional anymore. 4️⃣ “Temporary” environments still running → Dev, staging, test all on, all the time → No shutdown policies → Everyone assumed someone else would clean up Build expiry rules into the workflow. 5️⃣ No cost visibility for devs → Engineers never saw the AWS bill → No budgets, no alerts → No incentive to optimize Show the numbers. Make cost part of sprint reviews. Here’s the truth: AWS isn’t expensive. Messy teams make it expensive. We’ve helped teams save 30–60% → No downtime → No code changes → No extra tools Spending $1K+ on AWS? Drop a “review” below or DM me. We’ll find the leaks fast.

Kubernetes Cost Optimization: The $50K Lesson Our monthly AWS bill hit $80K. Leadership asked: "Why so expensive?" The answer wasn't pretty. We were running Kubernetes like it was free. Here's how we cut costs by 60% without sacrificing performance: 1. Right-Sizing Workloads Problem: Developers requesting 4GB RAM, using 400MB Solution: Vertical Pod Autoscaler + resource usage analysis Savings: 35% on compute costs 2. Spot Instances for Non-Critical Workloads Problem: Running dev/staging on expensive on-demand instances Solution: Karpenter for intelligent spot instance management Savings: 70% on non-production environments 3. Cluster Autoscaling Tuning Problem: Nodes spinning up too aggressively, staying idle Solution: Adjusted scale-down delay, implemented pod disruption budgets Savings: 20% reduction in idle node time 4. Storage Optimization Problem: Persistent volumes never deleted, snapshots piling up Solution: Automated PV cleanup policies, snapshot lifecycle management Savings: $8K/month on EBS costs alone 5. Multi-Tenancy with Namespaces Problem: Separate clusters for each team Solution: Consolidated to shared clusters with proper isolation Savings: Reduced cluster overhead by 40% 6. Reserved Instances for Stable Workloads Problem: Paying on-demand prices for always-running services Solution: 1-year RIs for baseline capacity Savings: 30% on predictable workloads Tools that helped: • Kubecost for cost visibility per namespace/pod • Karpenter for intelligent node provisioning • Prometheus metrics for usage analysis • AWS Cost Explorer for trend analysis The real win? Making cost a first-class metric alongside performance and reliability. Now every team sees their infrastructure spend in real-time. Cost awareness became part of the development culture. Final monthly bill: $32K Savings: $48K/month = $576K annually Kubernetes isn't expensive. Unoptimized Kubernetes is. What's your biggest cloud cost challenge? #Kubernetes #CloudCost #DevOps #AWS #CostOptimization #FinOps #CloudEngineering #InfrastructureEngineering #SRE #K8s

We inherited a messy EKS setup burning $25K/month. 😬 After 6 months of cleanup, we’re now saving over $100K a year. Here’s how we did it (and what actually worked): 🔧 1. Dev & Staging 24/7? Oops. We were running non-prod environments all the time. ✅ Added off-hours autoscaling = $3K/month saved. 🧠 2. One-size-fits-none Worker Nodes Everything ran on m5.2xlarge by default. ✅ Split workloads by resource profile (Go vs Java) = 35% EC2 cost cut. 💸 3. Spot Instances (The Right Way) Our first “go all-in” attempt? Disaster. ✅ Now we use them only for stateless workloads + proper fallbacks. 📦 4. Storage Wasteland Dev teams were requesting 100GB volumes by default. ✅ Switched to gp3 + added quotas = $3K/month saved. 📉 Results? 💵 AWS Bill: Down from $25K → $15K/month ⚡️ Perf: Improved 😴 Team: Sleeping better Top lessons: Monitor before you optimize Don’t over-optimize all at once Involve devs—they know their apps best Next up: Graviton2 testing (early signs say another 20% savings 👀). What’s your biggest EKS cost-saving win or horror story? Drop it below 👇 Let’s learn from each other. #AWS #EKS #DevOps #CloudCostOptimization #Kubernetes #CloudComputing #PlatformEngineering #Infrastructure #SRE #TechLeadership #SRE #DevOpsEngineer #FinOps #CloudInfra #SRE #EngineeringLeadership #CloudNative #CostEfficiency #TechOptimization #AWSBilling #Monitoring #Observability #PerformanceEngineering #EC2 #Terraform #Prometheus #SpotInstances #StorageOptimization #Graviton2 #CloudSavings #InfrastructureStrategy #CloudEngineering #EngineeringExcellence #DevOpsLife #TechWins #CloudStrategy