Nizar Al-Ramadeen’s Post

This is good checklist to follow for every Aws cloud project. Save resources use what you need and trim fat. Nat gateway is tough though so many APIs go through there. But if your s3 traffic is not optimized and going through nat gateway it can get expensive fast. It’s good to know db traffic is so low in this example that you can reduce it. I wouldn’t choose to lower that piece too much because you usually have one db and a failover you want high availability and high performance on the db.

AWS Backup looks simple on the bill until you break down all the meters. Most teams only watch backup storage. But total AWS Backup cost is usually a combination of: • Backup storage (warm/cold tiers, resource-dependent) • Cross-Region or Cross-Account copy storage + transfer • Restore usage (what you restore and how often) • Underlying service-specific effects (for example, retention patterns and lifecycle choices) The expensive part is rarely a single giant backup. It is usually policy drift over time: - retention periods that were never revisited - frequent cross-Region copies that no longer match RTO/RPO needs - restores used as “routine operations” instead of exception workflows - old recovery points that no one is actively validating The practical fix: Audit backup plans monthly by workload criticality, not once at setup. If your recovery policy changed but your retention/copy settings did not, you are likely paying for yesterday’s risk model. Cost optimization in backup is not “store less.” It is “store intentionally.” #AWS #AWSBackup #CloudCostOptimization #FinOps #CloudEconomics #DevOps #SRE #PlatformEngineering #CloudGovernance #BuildInPublic

A $47,000 mistake hiding in plain sight. We audited a SaaS company's AWS bill last month. Everything looked normal to them. Until we checked their RDS database instances. They were running 8 production databases on db.r5.4xlarge instances. $1.20/hour each. Running 24/7. Actual usage during analysis: 15-25% CPU utilization. Consistently. Here's what they should have been using: db.r5.xlarge instances at $0.30/hour each. The math: - Current cost: $7,040/month ($1.20 × 8 × 730 hours) - Right-sized cost: $1,752/month ($0.30 × 8 × 730 hours) - Waste: $5,288/month - Annual waste: $63,456 This took us 2 hours to identify and 4 hours to migrate with zero downtime. Total savings: $47,000 over 9 months remaining in their fiscal year. The pattern we see constantly: Teams select "safe" instance sizes during initial setup, then never revisit the decision. Development becomes production. Traffic doesn't scale as expected. Requirements change. But the infrastructure? It stays the same. And keeps charging you. Quick question: When was the last time you reviewed your database instance sizes? If the answer is "never" or "I'm not sure," you're probably overpaying. #CloudOptimization #AWS #CloudCosts #Enterprise #IT

Efficiency isn't just about moving faster, it's about stopping the "invisible" leaks in your OpEx. Most companies treat their cloud bill like a utility: pay it and move on. But as this audit shows, "business as usual" can cost you $47,000 in pure waste before your next quarterly review. If your infrastructure hasn't been right-sized since your last deployment, you aren't just paying for performance, you’re paying for air. This is why we advocate for continuous optimization. What’s the biggest 'hidden' cost you've found in a cloud audit?

Santosh Vallurupalli, Senior Solution Architect at Amazon Web Services (AWS), addresses the common challenge enterprises face when scaling Kubernetes backup management across multiple regions. He explains how organizations quickly accumulate individual backup policies (one per cluster) that become unmanageable as they grow to 50 or more clusters. Santosh recommends a policy-driven approach that uses tagging-based strategies to categorize clusters by priority levels (high, medium, low). This framework enables teams to adjust backup schedules, retention policies, and cross-region requirements according to business criticality, rather than managing individual cluster policies. Watch the full interview: https://ku.bz/pklYlRr80

🚨 AWS S3 Disruption – ME-CENTRAL-1 Region When one region goes down… everything depending on it feels the impact. 🔴 High S3 failure rates 🔴 Console & CLI disruptions 🔴 AZ outages (mec1-az2, mec1-az3) This is a strong reminder: 👉 Single Region = Single Point of Failure Even though AWS provides high availability within a region, real resilience means thinking beyond one region. ✅ Enable Multi-Region replication ✅ Design cross-region failover ✅ Test your Disaster Recovery plan regularly ✅ Don’t wait for an outage to validate architecture Cloud is reliable. Architecture makes it resilient. Are you truly multi-region ready? #AWS #AmazonS3 #CloudOutage #DisasterRecovery #CloudResilience #MultiRegion #HighAvailability #CloudArchitecture #DevOps #SRE #CloudEngineer #Infrastructure #SiteReliability #Failover #BusinessContinuity

Running MS SQL Server on EC2 looks cheaper than RDS… until you calculate operating costs. Licensing, standby costs, DR replication, patching, and operational overhead can quietly double your annual spend. And that’s before you factor in high availability and support limitations. In this blog by Shaswat vashistha, he breaks down: • EC2 cost components • Licensing cost impact • Annual cost with/without DR • RDS pricing and trade-offs It is a must read for teams struggling to choose between cost control and operational simplicity. Read more: https://lnkd.in/gvcPRMfZ #AWSCostOptimization #CloudCostManagement #AWSRDS #AmazonEC2 #MSSQLServer #FinOps #CloudArchitecture #DevOps #CloudSavings #CloudStrategy

A staging environment was costing $6,800/month. After reviewing it, we found several issues: - Staging was running 24/7 but only used about 10 hours per week - The database mirrored production with 3 years of copied data - Several unused services were still running Changes we implemented: - Staging now runs 9 AM – 6 PM on weekdays using Terraform scheduled scaling - The database is restored from a snapshot on demand instead of maintaining a live replica - Removed unused services New cost: $980/month Savings: $5,820/month No impact on the development workflow. One of the biggest cloud cost leaks is environments that sit idle but still bill like they're busy. #CloudComputing #CloudCostOptimization #DevOps #Infrastructure #Terraform #AWS #TechLeadership #FinOps

Designing a High Availability Infrastructure on AWS High Availability is built on these three pillars: 1. Redundancy: Multi-AZ setup for every component 2. Detection: ALB health checks, CloudWatch monitoring 3. Automated Recovery: ASG instance replacement, database failover, automated rollback. With these in place, users experience minimal to zero downtime even when things go wrong. 1. Multi-AZ API Servers: The Auto Scaling Group is configured to span multiple AZs (e.g. eu-west-1a and eu-west-1b). If one AZ goes down, the ASG automatically launches replacement instances in the healthy AZ. The ALB is multi-AZ by default so traffic reroutes almost instantly, users barely notice anything happened. 2. RDS Multi-AZ: A standby replica is kept in sync with the primary through synchronous replication (every write to the primary is simultaneously written to the standby in real-time). If the primary dies, the standby gets promoted within 60–120 seconds with zero data loss. 3. ElastiCache Redis with Automatic Failover: Without HA configured, if the Redis node goes down, data is lost because it's stored in memory, this results into a fallback to the RDS for data read, slowing everything down. With Multi-AZ and automatic failover, a replica in another AZ gets promoted when the primary fails. 4. ALB Health Checks: The ALB sends HTTP requests to a health endpoint on each target (e.g. GET /health every 30 seconds). If an instance fails a set number of consecutive checks, the ALB marks it unhealthy and stops routing traffic to it. The ASG then terminates the failed instance and launches a replacement automatically. The system heals itself without anyone intervening. 5. Automated Rollbacks: CloudWatch alarms monitor key metrics like error rates and latency on the ALB. These alarms are integrated with the deployment pipeline. Using a blue-green deployment strategy, traffic shifts to the new environment after deploy. CloudWatch monitors for a configured evaluation period. If error rates or latency exceed set thresholds, the alarm triggers and the deployment service automatically rolls back, shifting traffic back to the previous environment. No human intervention needed. This is how you solve the problem of bad code getting deployed at 3am when nobody is awake. #DevOps #AWS #HighAvailability #CloudArchitecture

Successfully Migrated Production Workloads from ECS (EC2) to AWS Fargate I recently led a production migration of our containerized application from ECS with EC2 launch type to AWS Fargate, modernizing our infrastructure and reducing operational overhead. This migration was focused on improving scalability, simplifying infrastructure management, and enhancing overall reliability. ✅ Key Benefits Achieved: - Eliminated EC2 instance management (no patching, AMIs, or capacity planning) - Improved scalability with task-level auto scaling - Reduced infrastructure complexity - Strengthened security using task-level IAM roles - Enabled more efficient, cloud-native architecture 🔧 What Was Involved: Migrating ECS services from EC2-backed clusters to Fargate Updating task definitions and execution roles Reconfiguring load balancers and target groups Reviewing and optimizing auto-scaling policies Ensuring zero-downtime deployment in production Validating performance and cost impact post-migration Always excited to work on initiatives that modernize platforms and improve operational efficiency. Thank you So much #CSA Team and Leaders for your help and support!! #AWS #ECS #Fargate #DevOps #CloudArchitecture #ProductionMigration #PlatformEngineering