Cloud Infrastructure Optimization

Alongside building resilient, highly available systems and strengthening security posture, I’ve been exploring a new focus area, optimising cloud costs. Over the last few months, this has led to some clear lessons for me that are worth sharing. 1. Compute planning is the foundation. Standardising on machine families and analysing workload patterns allows you to commit to savings plans or reserved instances. This is often the highest ROI move, delivering big savings without actually making a lot of technical changes. 2. Account structures impact cost. Multiple AWS accounts improve governance and security but make it harder to benefit from bulk discounts. Using consolidated billing and commitment sharing across accounts brings the efficiency back. 3. Kubernetes compute checks are important. Nodes in K8s are often over-provisioned or underutilised. Automated rebalancing tools help, as does smart use of spot instances selected for reliability. On top of this, workload resizing during off hours, reducing CPU and memory when demand is low, delivers direct and recurring savings. 4. Watch for operational leaks. Debug logs on CDNs and load balancers, once useful, often stay enabled long after issues are fixed. They quietly pile up costs until someone takes notice. 5. Right-sizing is a continuous process. Urgent projects often lead to overprovisioned instances for anticipated load that never fully arrives. Monitoring and regular reviews are the only way to keep infrastructure aligned with reality. The real win in cloud cost optimisation comes from treating it as a continuous practice, not a one-off project. Small inefficiencies compound fast, so important to be on the lookout! #CloudCostOptimization #AWS #Kubernetes #DevOps #CloudInfrastructure #RightSizing #WorkloadManagement #SavingsPlans #SpotInstances #CloudEfficiency #TechInsights #CloudOps #CostManagement #CloudBestPractices

Most teams assume reducing cloud costs means sacrificing performance. This case proves otherwise. A growing SaaS company was struggling with rising infrastructure costs, touching nearly $18K/month. Alongside this, their Kubernetes clusters were over-provisioned, and CI/CD pipelines were inefficient—causing unnecessary compute usage and slower deployments. The approach was simple but strategic. First, infrastructure was optimized by right-sizing resources, enabling autoscaling, and leveraging spot instances. Next, CI/CD pipelines were enhanced using caching and parallel execution, significantly reducing build times. Finally, cost visibility was introduced through monitoring dashboards and alerting systems. The impact was immediate and measurable. Cloud costs dropped by 38%, bringing expenses down to around $11K/month. Deployment speeds doubled, and teams gained real-time visibility into their infrastructure spend. The biggest takeaway? Cloud waste isn’t just a technical issue—it’s a visibility and ownership problem. When teams understand where resources are being used, optimization becomes natural. If your cloud bill is scaling faster than your product, it’s time to rethink your architecture—not your budget. #CloudComputing #DevOps #AWS #Kubernetes #CostOptimization #SRE #Infrastructure #TechLeadership #CI_CD #StartupTech

𝐌𝐨𝐬𝐭 𝐨𝐫𝐠𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧𝐬 𝐝𝐨𝐧'𝐭 𝐡𝐚𝐯𝐞 𝐚 𝐜𝐥𝐨𝐮𝐝 𝐜𝐨𝐬𝐭 𝐩𝐫𝐨𝐛𝐥𝐞𝐦. 𝐓𝐡𝐞𝐲 𝐡𝐚𝐯𝐞 𝐚 𝐜𝐥𝐨𝐮𝐝 𝐯𝐢𝐬𝐢𝐛𝐢𝐥𝐢𝐭𝐲 𝐩𝐫𝐨𝐛𝐥𝐞𝐦. Cloud bills don't suddenly become expensive. They grow because no one knows where the money is going until it's too late. 💰 In 2026, AWS cost optimization is no longer about finding discounts. It's about building a FinOps operating model powered by the right tools. 𝐇𝐞𝐫𝐞'𝐬 𝐡𝐨𝐰 𝐥𝐞𝐚𝐝𝐢𝐧𝐠 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐭𝐞𝐚𝐦𝐬 𝐚𝐩𝐩𝐫𝐨𝐚𝐜𝐡 𝐢𝐭: 📊 Native AWS Visibility • Track spend with Cost Explorer • Set proactive budgets and alerts • Detect anomalies before they become surprises • Use optimization recommendations continuously ⚙️ Rightsizing & Waste Elimination • Identify idle resources • Rightsize compute automatically • Eliminate unused storage and services 🤝 Commitment Optimization • Maximize Savings Plans and Reserved Instances • Automate commitment management • Continuously balance cost and utilization ☸️ Kubernetes & EKS Cost Control • Allocate costs by team and workload • Optimize clusters automatically • Improve container efficiency 🏢 Enterprise Governance • Standardize FinOps across multiple accounts and clouds • Enforce policies and cost accountability • Align engineering with business objectives 📈 Unit Economics & FinOps • Measure cost per customer, product, and feature • Track engineering decisions against business value • Make cloud spend visible to every team 🚀 Shift Cost Left • Estimate infrastructure costs before deployment • Embed cost awareness into CI/CD pipelines • Prevent expensive architecture decisions early 💡 The biggest shift? Cloud cost optimization is no longer a finance exercise. It's an engineering capability. The organizations reducing cloud spend the fastest aren't cutting infrastructure. They're improving visibility, governance, automation, and accountability across the entire cloud lifecycle. Because you can't optimize what you can't see. Follow Sneha Konnur for more insights

30% of cloud spending is wasted due to inefficiencies. I keep seeing the same pattern in AVD environments. VMs overprovisioned "just to be safe". Auto-scaling policies that were never actually configured. Storage accounts nobody's looked at in months. Meanwhile, finance is questioning every Azure invoice. Applying DevOps principles to your cloud desktop environment genuinely fixes this: 🔹 Infrastructure as Code - Use Terraform, Bicep or Nerdio Manager to automate resource provisioning. When infrastructure is code, environments become reproducible, auditable, and cost-optimised by default. No more inconsistent deployments that drift and accumulate waste. 🔹 Automated Scaling - Configure Nerdio AVD scaling plans properly. Enable Start VM on Connect so session hosts stay deallocated until users actually need them. You only pay for compute when someone's working. 🔹 Continuous Monitoring - Azure Monitor or Nerdio Manager autoscaling history gives you visibility into usage patterns. Once you have that data, you can identify which host pools are overprovisioned and which storage accounts are burning money overnight. 🔹 Right-Sizing Resources - Match VM SKUs to actual workload requirements. I've seen customers running D16S for users who barely touch 4 vCPUs. That's expensive guesswork. Use metrics to validate your sizing decisions. 🔹 Regular Cost Audits - Schedule quarterly reviews of your cloud resources. Orphaned disks, unattached public IPs, oversized FSLogix storage tiers... these accumulate quietly and compound monthly. 🔹 Automation Tooling - Nerdio Manager for Enterprise automates much of this for AVD. Intelligent autoscaling, cost reporting, right-sizing recommendations. Takes the manual effort out of continuous optimisation. The organisations I work with that treat cloud desktop infrastructure as code rather than clicking through portals consistently see material cost reductions. Most teams know what to do - actually implementing it consistently is where things fall apart. What's the biggest cost-waste generator you've found in your environment? #AVD #DevOps #Azure #Nerdio #FinOps #AzureVirtualDesktop #Azure #Nerdio

We replaced AWS ALB with 1990s tech — and handled 10× more traffic for $0.01/hour. Sounds insane. It isn’t. Our Application Load Balancers were quietly eating $3,800/month just to forward packets. Latency was fine. Reliability was fine. But the cost-to-value ratio made no sense anymore. So we did something most teams don’t even consider in 2026: We removed ALB entirely Moved load balancing into the Linux kernel Used IPVS (yes, that IPVS) What changed Instead of managed L7 load balancers, we run: • IPVS as a Kubernetes DaemonSet • One tiny node per AZ • Elastic IPs via kube-vip • Direct Server Return (DSR) Result: • 10× higher throughput • Sub-millisecond connection setup • No LCU tax • No proxying response traffic • $0.009/hour per AZ The load balancer stopped being the bottleneck. “But IPVS is dumb L4” Exactly. That’s the point. We push intelligence inward, not outward: • L4 performance at the edge (IPVS) • L7 routing via Envoy inside the pod • Kernel speed where it matters • Flexibility where it belongs The real takeaway Managed ≠ optimal. AWS load balancers are amazing for: • Fast setup • Generic workloads • Default architectures They are not optimized for: • High-throughput systems • Cost-disciplined platforms • Teams that know their traffic patterns We traded a few hours of setup for: • ~$45K/year savings • Better latency • Full control of the data path Sometimes the most “cloud-native” move is remembering how systems worked before abstraction hid the costs. Curious what others think Would you ever drop managed LBs in production — or is this a step too far? #AWS #DevOps #Kubernetes #CloudArchitecture #SiteReliabilityEngineering #Infrastructure #CostOptimization #PlatformEngineering #Linux #Networking #Scaling Beacon Hill TEKsystems Randstad Digital Americas Northern Trust

Most companies optimize cloud costs by focusing on the wrong part of the equation. Here's the formula that drives every cloud bill: Cloud Cost = Usage × Price Most FinOps teams attack the price component: - Negotiate enterprise agreements with AWS, Azure etc - Buy reserved instances for discounts - Commit to spending quotas for better rates You can get 60% off through aggressive pricing negotiations, but here's the problem: If an engineer launches a server and never uses it, that's 100% waste. Even with a 60% discount, you're still wasting 40%. The better strategy: Optimize usage first, then negotiate price. → Get your $30M annual spend down to $10M through better resource utilization. → Then go to AWS and negotiate 10% off that $10M instead of negotiating 20% off the wasteful $30M. The usage component is entirely in engineers' hands: - What services do they choose? - How do they configure them? - How much CPU and memory? But companies avoid this because it's harder. Most take the easy path and just negotiate with vendors. That's why we built Infracost at the usage layer - it's where the real optimization happens.

Unlocking the Secrets of Cloud Costs: Small Tweaks, Big Savings! Three fundamental drivers of cost: compute, storage, and outbound data transfer. 𝐂𝐨𝐬𝐭 𝐎𝐩𝐬 refer to the strategies and practices for managing, monitoring, and optimizing costs associated with running workloads and hosting applications on provider’s infrastructure. 𝐖𝐚𝐲𝐬 𝐭𝐨 𝐌𝐢𝐧𝐢𝐦𝐢𝐳𝐞 𝐂𝐥𝐨𝐮𝐝 𝐇𝐨𝐬𝐭𝐢𝐧𝐠 𝐂𝐨𝐬𝐭𝐬: 💡𝐑𝐢𝐠𝐡𝐭-𝐒𝐢𝐳𝐢𝐧𝐠 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬: 📌 Ensure you're using the right instance type and size. Cloud providers offer tools like Compute Optimizer to recommend the right instance size. 📌 Implement auto-scaling to automatically adjust your compute resources based on demand, ensuring you're only paying for the resources you need at any given time. 💡𝐔𝐬𝐞 𝐒𝐞𝐫𝐯𝐞𝐫𝐥𝐞𝐬𝐬 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞𝐬: 📌 Serverless solutions like AWS Lambda, Azure Functions, or Google Cloud Functions allow you to pay only for the execution time of your code, rather than paying for idle resources. 📌 Serverless APIs combined with functions can help minimize the need for expensive always-on infrastructure. 💡𝐔𝐭𝐢𝐥𝐢𝐳𝐞 𝐌𝐚𝐧𝐚𝐠𝐞𝐝 𝐒𝐞𝐫𝐯𝐢𝐜𝐞𝐬: 📌 If you're running containerized applications, services like AWS Fargate, Azure Container Instances, or Google Cloud Run abstract away the management of servers and allow you to pay for the exact resources your containers use. 📌 Use managed services like Amazon RDS, Azure SQL Database, or Google Cloud SQL to lower costs and reduce database management overhead. 💡𝐒𝐭𝐨𝐫𝐚𝐠𝐞 𝐂𝐨𝐬𝐭 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: 📌 Use the appropriate storage tiers (Standard, Infrequent Access, Glacier, etc.) based on access patterns. For infrequently accessed data, consider cheaper options to save costs. 📌 Implement lifecycle policies to transition data to more cost-effective storage as it ages. 💡𝐋𝐞𝐯𝐞𝐫𝐚𝐠𝐞 𝐂𝐨𝐧𝐭𝐞𝐧𝐭 𝐃𝐞𝐥𝐢𝐯𝐞𝐫𝐲 𝐍𝐞𝐭𝐰𝐨𝐫𝐤𝐬 (𝐂𝐃𝐍𝐬): Using CDNs like Amazon CloudFront, Azure CDN, or Google Cloud CDN can reduce the load on your backend infrastructure and minimize data transfer costs by caching content closer to users. 💡𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 𝐚𝐧𝐝 𝐀𝐥𝐞𝐫𝐭𝐬: Set up monitoring tools such as CloudWatch, Azure Monitor etc. to track resource usage and set up alerts when thresholds are exceeded. This can help you avoid unnecessary expenditures on over-provisioned resources. 💡𝐑𝐞𝐜𝐨𝐧𝐬𝐢𝐝𝐞𝐫 𝐌𝐮𝐥𝐭𝐢-𝐑𝐞𝐠𝐢𝐨𝐧 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭𝐬: Deploying applications across multiple regions increases data transfer costs. Evaluate if global deployment is necessary or if regional deployments will suffice, which can help save costs. 💡𝐓𝐚𝐤𝐞 𝐀𝐝𝐯𝐚𝐧𝐭𝐚𝐠𝐞 𝐨𝐟 𝐅𝐫𝐞𝐞 𝐓𝐢𝐞𝐫𝐬: Most cloud providers offer free-tier services for limited use. Amazon EC2, Azure Virtual Machines, and Google Compute Engine offer limited free usage each month. This is ideal for testing or running lightweight applications. #cloud #cloudproviders #cloudmanagement #costops #tech #costsavings

If you’re in cloud and not looking at optimization end-to-end, you’re missing out — here are the key strategies you should know.. → Compute ↳ Right-size instances, use auto-scaling/serverless, and leverage spot/preemptible VMs ↳ Consolidate workloads with Kubernetes/Fargate/Cloud Run → Storage ↳ Use lifecycle policies to move infrequently used data to cheaper tiers ↳ Deduplication, compression, and smart replication strategies reduce costs → Networking ↳ CDN for static content, private networking to cut egress, and traffic shaping with load balancers ↳ Always optimize data transfer (avoid unnecessary cross-region costs) → Databases ↳ Use managed services, read replicas, and caching ↳ Shard/partition for scale, and pick the right DB for the workload → Big Data ↳ Spot clusters for jobs, serverless analytics, and data partitioning ↳ Stream only what’s critical, batch the rest → Security ↳ Enforce least privilege IAM, encrypt in transit/at rest ↳ Automate threat detection and centralize secrets with KMS/Vault → AI/ML ↳ Track experiments, use AutoML/pre-trained APIs ↳ Share GPUs, and clean/optimize data before training Essential Note: Cloud optimization isn’t a one-time exercise. You have to keep at it — especially now, with AI workloads driving cloud costs to new highs. Start with one area → measure impact → repeat. What other strategies would you add? • • • If you found this useful.. 🔔 Follow me (Vishakha) for more Cloud & DevOps insights ♻️ Share so others can learn as well!

9 𝐏𝐨𝐰𝐞𝐫𝐟𝐮𝐥 𝐖𝐚𝐲𝐬 𝐭𝐨 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐞 𝐘𝐨𝐮𝐫 𝐂𝐥𝐨𝐮𝐝 𝐟𝐨𝐫 𝐄𝐧𝐡𝐚𝐧𝐜𝐞𝐝 𝐃𝐞𝐯𝐎𝐩𝐬 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 Looking to streamline your DevOps workflow and optimize your cloud environment? This post dives into 9 impactful strategies that will empower your DevOps team to achieve optimal cloud performance 1️⃣Right-sizing Your Cloud Footprint - Match your services to your workload requirements. Don't overspend on excessive resources or struggle with underprovisioning that hinders performance. - Continuously monitor and analyze your cloud usage to ensure you're utilizing resources effectively. 2️⃣Leveraging Auto-Scaling for Dynamic Resource Allocation - Automate scaling policies to adjust resources based on real-time demand. This helps optimize costs during low-traffic periods and prevents bottlenecks during peak usage. 3️⃣ Embracing Cloud-Native Architecture with Microservices - Build applications as a collection of small, independent microservices. This enables independent scaling for each service, leading to precise resource allocation and increased flexibility. 4️⃣ Optimizing Data Storage for Efficiency and Cost-Effectiveness - Implement tiered storage based on data access frequency. Frequently accessed data can reside on high-performance storage, while less frequently accessed data can be stored on cost-efficient tiers. - Utilize data compression and deduplication techniques to minimize storage needs and associated costs. 5️⃣ Implementing Proactive Cost Management - Maintain close track of your cloud spending with detailed cost analysis tools provided by your cloud provider. - Set up budget alerts and notifications to avoid unexpected charges and maintain financial control. 6️⃣ Exploring Multi-Cloud Strategies for Enhanced Benefits - This approach can also improve disaster recovery capabilities by ensuring redundancy across different cloud environments. 7️⃣ Implementing Effective Caching Strategies for Faster Data Retrieval - Deploy caching mechanisms to store frequently accessed data temporarily, reducing server load and improving application responsiveness. - Explore edge caching to store data closer to users for geographically distributed applications. - Utilize in-memory caching to store frequently accessed data in server memory for lightning-fast retrieval. 8️⃣ Leveraging Managed Services for Expert Support and Resource Efficiency - Offload day-to-day management tasks to your cloud provider's managed services, freeing up your DevOps team to focus on core development activities. 9️⃣ Adopting Infrastructure as Code (IaC) for Automation and Consistency - Manage and provision your cloud infrastructure through code (IaC). This enables automated infrastructure deployments, reduces manual errors, and ensures consistency across your cloud environment. - IaC also simplifies scaling processes by allowing updates to infrastructure code to reflect changes in resource requirements.

𝐊𝐮𝐛𝐞𝐫𝐧𝐞𝐭𝐞𝐬 𝐂𝐨𝐬𝐭 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐓𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬 Kubernetes, a powerful container orchestration platform, can significantly reduce costs when used effectively. Here are some key strategies for optimizing your Kubernetes environment: 1. Rightsizing and Resource Allocation Pod Limits and Requests: Set precise resource limits and requests for each pod to prevent over-allocation and under-utilization. Node Sizing: Choose the appropriate node size based on your workload requirements to avoid paying for excess resources. Horizontal Autoscaling: Automatically scale pods up or down based on demand to ensure optimal resource utilization. Vertical Autoscaling: Adjust the resource allocation for pods to match their workload requirements. 2. Cost Monitoring and Analysis Utilize Cloud Provider Tools: Leverage cloud-specific tools (e.g., AWS Cost Explorer, GCP Cost Management) to track spending and identify cost-saving opportunities. Third-Party Tools: Consider using tools like Kubecost or Prometheus for detailed cost analysis and visualization. Regular Reviews: Regularly review your cost data to identify trends and areas for optimization. 3. Spot Instances and Preemptible VMs Leverage Spot Instances: Use spot instances or preemptible VMs for non-critical workloads to significantly reduce costs. Implement Fault Tolerance: Ensure your applications can handle interruptions caused by spot instance terminations. 4. Image Optimization Minimize Image Size: Remove unnecessary files and layers from your container images to reduce download and storage costs. Use Multi-Stage Builds: Create optimized images by building in multiple stages and copying only necessary artifacts. 5. Network Optimization Network Policies: Use network policies to restrict traffic between pods and reduce unnecessary network traffic. Load Balancing: Implement efficient load balancing strategies to distribute traffic evenly across pods. 6. Storage Optimization Persistent Volume Claims (PVCs): Use PVCs to manage persistent storage efficiently and avoid over-provisioning. Storage Classes: Create storage classes to define different storage types and their associated costs. Storage Provisioners: Choose appropriate storage provisioners based on your workload requirements and cost considerations. 7. Cluster Sharing Consolidate Clusters: If possible, consolidate multiple clusters into a single, shared cluster to reduce overhead costs. Namespace Isolation: Use namespaces to logically isolate different workloads within a shared cluster. 8. Consider Managed Kubernetes Services Evaluate Managed Offerings: Explore managed Kubernetes services (e.g., EKS, GKE, AKS) that often provide cost-effective solutions and managed infrastructure. Check here for more kubernetes Projects - https://lnkd.in/g5jCpiQg Share this post with your devops friends :)