Cloud Computing Cost Efficiency Strategies

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

In my last year at AWS, I was once tasked with finding $400 million in cost savings for cloud spending in just one year. It was a daunting challenge, but I learned a lot of valuable lessons along the way that I'd like to share with you. First, let's go over what I did to save that $400 million. Here are the top three strategies that worked for me: - Automation of idle instances: It's common for developers and testers to leave instances running even when they're not being used, which can add up quickly. We built automation to identify idle instances, tagged them, sent emails to people, and shut them down automatically if we didn’t get a response to leave them up. - Elimination of unused backups and storage: We found that we were keeping backups of customer data that we weren't using, which was costing us a lot of money. By reaching out to customers and getting their approval to delete backups that weren't being used, we were able to save a substantial amount of money. - Reserved instances: Reserved instances have a much lower cost than on-demand instances, so we made sure to buy them whenever possible. We also used convertible RIs so that we could shift between instance types if there were mispredictions about which types of instances would be in demand. Now, let's talk about what I would do differently if I were facing this challenge today. Here are two key strategies that I'd focus on: - Start with automation: As I mentioned earlier, automating the identification and shutdown of idle instances is crucial for cost savings. I'd make sure to start with this strategy right away, as it's one of the easiest and most effective ways to save money. - Be cautious with reserved instances: While RIs can be a great way to save money, they're not always the right choice. If you're in a world where you might be shrinking, not growing, you need to be much more cautious about buying RIs. Make sure to consider your commitment to buy and whether you'll be able to sell the capacity later. What would you add to this list? #devops #cloud #automation

A modernization journey to Cloud Native has #cost benefits. #Cloud-native container environments are typically more cost-effective than VM-based environments due to better resource utilization, scalability, and automation features. Resource Utilization: #Containers: Containers generally use fewer resources than VMs because they share the host OS, resulting in less overhead. This allows running more applications on the same hardware, reducing overall costs. VMs: Each VM requires a full OS installation, leading to higher overhead and resource consumption. This results in fewer applications per host and potentially higher costs. #Pricing Models: AWS and Azure both offer pay-as-you-go models, but containers can be run on services like AWS ECS or EKS and Azure AKS, where resources scale dynamically based on demand, leading to cost savings. VMs are generally priced by size (vCPU, memory) and duration of use, leading to more predictable but often higher costs due to unused, idle capacity. #Scalability and Elasticity: Containers: Both #AWS Fargate and #Azure Kubernetes Service (AKS) support autoscaling, allowing containers to scale in real-time, optimizing cost efficiency by only using resources when needed. VMs: While VMs can be manually scaled or automatically through certain cloud services, they are slower to scale and often over-provisioned, leading to increased costs. #Maintenance Costs: Containers: Offer a serverless container option (e.g., AWS Fargate, Azure Container Instances) that offloads infrastructure management, potentially lowering operational costs. VMs: Require more effort in management, patching, and monitoring, increasing operational overhead and costs. #Cost Comparison (AWS and Azure): AWS: For example, running a t3.medium EC2 instance costs approximately $0.0416 per hour, whereas running a container using AWS Fargate can start as low as $0.0126 per hour (for compute and memory). Azure: Similarly, a D2_v3 VM instance costs around $0.096 per hour, while Azure Container Instances might cost $0.000012 per GB and $0.000012 per vCPU per second, offering more granular billing and potential savings. Actionable Steps & Risks: #Analyze Workloads: For optimal cost efficiency, assess whether your workloads can benefit from containerized environments, especially for microservices or stateless applications. #Use Autoscaling: Implement autoscaling strategies for containers to dynamically adjust resource consumption based on real-time demand. #Monitor Hidden Costs: While containers reduce resource consumption, factor in networking, storage, and data transfer costs, which can vary depending on the cloud provider and setup. #Risk Mitigation: For mission-critical applications, ensure that the container management platform has robust monitoring, security, and backup strategies to avoid potential downtime or security breaches.

Imagine you’re filling a bucket from what seems like a free-flowing stream, only to discover that the water is metered and every drop comes with a price tag. That’s how unmanaged cloud spending can feel. Scaling operations is exciting, but it often comes with a hidden challenge of increased cloud costs. Without a solid approach, these expenses can spiral out of control. Here are important strategies to manage your cloud spending: ✅ Implement Resource Tagging → Resource tagging, or labeling, is important to organize and manage cloud costs. → Tags help identify which teams, projects, or features are driving expenses, simplify audits, and enable faster troubleshooting. → Adopt a tagging strategy from day 1, categorizing resources based on usage and accountability. ✅ Control Autoscaling → Autoscaling can optimize performance, but if unmanaged, it may generate excessive costs. For instance, unexpected traffic spikes or bugs can trigger excessive resource allocation, leading to huge bills. → Set hard limits on autoscaling to prevent runaway resource usage. ✅ Leverage Discount Programs (reserved, spot, preemptible) → For predictable workloads, reserve resources upfront. For less critical processes, explore spot or preemptible Instances. ✅ Terminate Idle Resources → Unused resources, such as inactive development and test environments or abandoned virtual machines (VMs), are a common source of unnecessary spending. → Schedule automatic shutdowns for non-essential systems during off-hours. ✅ Monitor Spending Regularly → Track your expenses daily with cloud monitoring tools. → Set up alerts for unusual spending patterns, such as sudden usage spikes or exceeding your budgets. ✅ Optimize Architecture for Cost Efficiency → Every architectural decision impacts your costs. → Prioritize services that offer the best balance between performance and cost, and avoid over-engineering. Cloud cost management isn’t just about cutting back, it’s about optimizing your spending to align with your goals. Start with small, actionable steps, like implementing resource tagging and shutting down idle resources, and gradually develop a comprehensive, automated cost-control strategy. How do you manage your cloud expenses?

Duolingo was sitting on a cloud cost ticking time bomb 💣. Here’s how they defused it. 1️⃣ 2.1 Billion Unnecessary API Calls! That’s right—BILLION. Features like stories, adventures, and DuoRadio scaled fast, but without efficient caching, service-to-service traffic spiraled out of control. ✔️ Duolingo’s Fix: Optimized caching mechanisms, slashing API calls by 60% and reclaiming performance. 2️⃣ Legacy Systems Wasting Resources Outdated clusters, unused databases, and redundant microservices added bloat. ✔️ Duolingo’s Fix: Decommissioned everything unnecessary, reallocating resources to high-priority workloads. 3️⃣ Staging Environments Costlier than Production An overlooked test configuration meant staging was draining more than it should. ✔️ Duolingo’s Fix: Enabled full cost visibility with CloudZero, identifying and resolving inefficiencies. 4️⃣ Overprovisioning Due to Poor Defaults Memory utilization was hovering way below optimal. ✔️ Duolingo’s Fix: Fine-tuned configurations for 90-95% memory use and migrated key databases to Aurora I/O-optimized instances. 💡 The Results? ✅ Service-to-service traffic dropped by 60%. ✅ Cloud costs shrank by 20% within months. ✅ Hundreds of thousands saved annually—on a single service! What’s the takeaway? Cloud optimization isn’t just about cost-cutting; it’s about building visibility, cleaning up tech debt, and maximizing efficiency. What’s your biggest win (or hurdle) in managing cloud costs? Let’s talk in the comments. P.S. Share this with your network so they can optimize smarter, too. ♻️ #duolingo #CloudOptimization #CostSavings #Scalability #Simform

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!

𝗖𝗹𝗼𝘂𝗱 𝗖𝗼𝘀𝘁 𝗥𝗲𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 Most cloud waste isn’t hidden. It’s right in front of you, idle resources, oversized machines, or systems running when no one needs them. Here are some ways to cut costs without hurting performance: 𝟭. 𝗟𝗶𝗳𝗲𝗰𝘆𝗰𝗹𝗲 𝗽𝗼𝗹𝗶𝗰𝗶𝗲𝘀 𝗳𝗼𝗿 𝘀𝘁𝗼𝗿𝗮𝗴𝗲. Move old data down to cheaper tiers automatically. Don’t keep backups or logs sitting in premium storage. 𝟮. 𝗛𝘆𝗯𝗿𝗶𝗱 𝗹𝗶𝗰𝗲𝗻𝘀𝗶𝗻𝗴. Bring your own Windows/SQL licenses. Providers charge a premium if you don’t. 𝟯. 𝗥𝗲𝘀𝗲𝗿𝘃𝗲 𝘁𝗼 𝗹𝗼𝘄𝗲𝗿 𝗿𝗮𝘁𝗲𝘀. Commit capacity for 1–3 years. If a workload is stable, this is easy money saved. 𝟰. 𝗧𝗲𝗿𝗺𝗶𝗻𝗮𝘁𝗲 𝗶𝗱𝗹𝗲 𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀. Kill VMs, disks, or clusters no one uses. Idle is the most expensive state. 𝟱. 𝗥𝗶𝗴𝗵𝘁-𝘀𝗶𝘇𝗶𝗻𝗴. Most apps don’t need the horsepower you first assign them. Shrink instances to fit reality. 𝟲. 𝗦𝗵𝘂𝘁𝗱𝗼𝘄𝗻 𝗶𝗻 𝗶𝗻𝗮𝗰𝘁𝗶𝘃𝗲 𝗵𝗼𝘂𝗿𝘀. Dev and test systems don’t need to run at 2 AM. Automate schedules to stop them. Extra places to look: 🔹 Use Spot/Preemptible instances for non-critical workloads 🔹 Optimize data transfer (CDNs, compression, clever placement) 🔹 Monitor daily waste shows up faster than you expect Cloud costs don’t get better by themselves. They get better when you take control.

Controlling Cloud Costs: A Strategic Imperative The benefits of moving to the cloud are well-documented—agility, scalability, and the ability to deliver solutions rapidly. These are key drivers of modernization for many organizations. However, the financial realities can be surprising if not actively managed. Cloud adoption often begins organically and can quickly become a significant expense if left unchecked. Managing these costs is no small task, but it is critical to address them early and effectively. Here are some strategies to consider: 1️⃣ Establish a FinOps Practice: Tagging and monitoring expenses ensures visibility. Regularly audit your resources to identify and shut down unused services that contribute to unnecessary spending. 2️⃣ Leverage Reserved Instances and Savings Plans: To optimize your costs, understand the differences and benefits of these offerings compared to on-demand pricing. 3️⃣ Reevaluate Workloads: Overprovisioning or failing to reassess workloads post-deployment can lead to inefficiencies. Regular evaluations and adopting hybrid or cloud-agnostic architectures can yield substantial savings. 4️⃣ Engage Cross-Functional Teams: Collaboration between finance, procurement, and engineering is crucial. A shared understanding of cloud cost dynamics fosters better decision-making. With intentional strategies, organizations can regain control over cloud spending and achieve cost optimization without compromising innovation. How is your organization managing cloud costs? Let’s exchange ideas and best practices to navigate this ever-evolving landscape.

If I were Head of FinOps of a SaaS company, here’s my 4-step playbook to cut up to 20% off our cloud costs, avoid expensive vendor lock-in, and align my entire company on cloud spending: This playbook is simple, but you’d be surprised how much the basics can help transform your bottom line. Here’s my playbook: 1. Understand your workloads You need to know what workloads you’re running and whether they’re predictable or dynamic. - Predictable If you have workloads that don’t change a lot – as in, you can forecast cloud costs accurately — lock in volume discounts like reserved instances or savings plans. - Dynamic If you have no idea what the resource profile of certain workloads will look like,  say you’re innovating, stick with on-demand capacity. You don’t want to risk overcommitting to enterprise discount pricing (EDP). For instance, if your actual spend is $70M but you commit to $250M, that’s a painful conversation with the CFO waiting to happen. 2. Stop running your engine overnight Instances running 24/7 without being used are a hidden cost killer. Implementing automated scheduling systems to power down these instances during periods of inactivity can significantly reduce costs. It’s like turning off your electric car overnight so you can drive it the next day without recharging. This may be straightforward. But at scale, this simple change can free up a significant budget. 3. Attached storage waste Storage utilization is often overlooked. One of our customers had a petabyte-sized S3 bucket costing $10k per month – yet no one knew what it was for. Right size your instances and audit storage usage regularly. Otherwise, you’re wasting resources like using a tank to kill a rat. 4. Make cost management a KPI Cloud cost visibility must be a company-wide priority – a top-level KPI so everyone knows they’re accountable. Focusing on this can lead to up to20% savings as people start paying attention to what’s being spent and why. Final thoughts: Cloud cost management is like fitness: every day counts. You won’t see the results immediately, but your expenses will balloon without consistent effort. Start today, focus on the basics, and watch your costs shrink over time. Pay now or pay later – the choice is yours.

Want to slash your EC2 costs? Here are practical strategies to help you save more on cloud spend. Cost optimization of applications running on EC2 can be achieved through various strategies, depending on the type of applications and their usage patterns. For example, is the workload a customer-facing application with steady or fluctuating demand, or is it for batch processing or data analysis? It also depends on the environment, such as production or non-production, because workloads in non-production environments often don't need EC2 instances to run 24x7. With these considerations in mind, the following approaches can be applied for cost optimization: 1. Autoscaling: In a production environment with a workload that has known steady demand, a combination of EC2 Savings Plans for the baseline demand and Spot Instances for volatile traffic can be used, coupled with autoscaling and a load balancer. This approach leverages up to a 72% discount with Savings Plans for predictable usage, while Spot Instances offer even greater savings, with up to 90% savings for fluctuating traffic. Use Auto Scaling and Elastic Load Balancing to manage resources efficiently and scale down during off-peak hours. 2. Right Sizing: By analyzing the workload—such as one using only 50% memory and CPU on a c5 instance—you can downsize to a smaller, more cost-effective instance type, such as m4 or t3, significantly reducing costs. Additionally, in non-production environments, less powerful and cheaper instances can be used since performance requirements are lower compared to production. Apply rightsizing to ensure you're not over-provisioning resources, incurring unnecessary costs. Use AWS tools like AWS Cost Explorer, Compute Optimizer, or CloudWatch to monitor instance utilization (CPU, memory, network, and storage). This helps you identify whether you’re over-provisioned or under-provisioned. 3. Downscaling: Not all applications need to run 24x7. Workloads like batch processing, which typically run at night, can be scheduled to shut down during the day and restart when necessary, significantly saving costs. Similarly, workloads in test or dev environments don't need to be up and running 24x7; they can be turned off during weekends, further reducing costs. 4. Spot Instances: Fault-tolerant and interruptible workloads, such as batch processing, CI/CD, and data analysis, can be deployed on Spot Instances, offering up to 90% savings over On-Demand instances. Use Spot Instances for lower-priority environments such as DEV and Test, where interruptions are acceptable, to save costs significantly. Cost optimization is not a one-time activity but a continual process that requires constant monitoring and reviewing of workload and EC2 usage. By understanding how resources are being used, you can continually refine and improve cost efficiency. Love to hear your thoughts-what strategies have you used to optimize your EC2 costs?