Virtualization vs Cloud Computing: What’s the Difference?

Green Clouds — cloud infrastructure that runs on renewable energy, minimizes idle waste, and actively tracks carbon output — have shifted from a sustainability buzzword to a board-level business requirement in 2026. If you are a CTO, CIO, or engineering leader evaluating cloud strategy, this guide gives you the frameworks, tools, and operational playbooks to make your cloud infrastructure measurably greener without sacrificing performance or cost efficiency. Global data center energy consumption now accounts for 2.5% of worldwide CO2 emissions — more than the aviation industry. Yet most organizations have no idea how much carbon their cloud workloads actually emit, let alone a plan to reduce it. That gap is exactly what green cloud computing addresses: shifting from good intentions to measurable, operational sustainability embedded directly into your infrastructure decisions. At Gart Solutions, we work with engineering teams across Europe and North America to make cloud infrastructure both cost-efficient and environmentally accountable. This article shares what we have learned — including the mistakes organizations consistently make, the tools that actually deliver results, and how to build a green cloud strategy that satisfies ESG reporting requirements without adding operational overhead. 80%+ Potential carbon reduction by migrating on-prem workloads to AWS (451 Research) 5.9% Estimated reduction in global IT emissions through widespread cloud adoption 2030 Target year for 24/7 carbon-free energy at Google; Azure carbon-negative; AWS net-zero The Environmental Impact of Cloud Computing Energy Consumption and Carbon Emissions Traditional cloud data centers, composed of extensive server farms, consume vast amounts of electricity. These centers often rely on fossil fuels, exacerbating greenhouse gas emissions. Reports suggest that the energy used by data centers worldwide accounts for approximately 1% of global electricity consumption, with this figure expected to rise​. Cooling Systems: A significant portion of energy usage in these data centers is attributed to cooling systems, which regulate server temperatures. Carbon Footprint: The reliance on non-renewable energy sources amplifies the environmental toll, contributing significantly to climate change. Resource Depletion and E-Waste Beyond energy concerns, the manufacturing and decommissioning of hardware lead to resource depletion and electronic waste (e-waste). An estimated 50 million tons of e-waste are generated globally each year, highlighting the urgency for sustainable lifecycle management of cloud infrastructure​. Water Usage Data centers also consume substantial amounts of water for cooling, which places stress on local water resources, further exacerbating their environmental footprint. Why Cloud is More Affordable Cloud computing transforms the landscape of IT services, moving away from traditional desktop setups to remote data centers. Users can effortlessly access on-demand infrastructure, eliminating the need for on-site installation and maintenance. Green cloud computing takes this concept a step further by utilizing renewable energy sources, reducing energy consumption, and making a significant dent in the carbon footprint. Virtualization and containerization, dividing hardware for deploying multiple operating systems, help reduce server needs and energy consumption. AI-based resource scheduling, guided by historical usage data, conserves energy. Infrastructure as a Service (IaaS) optimization, focusing on virtual machines and containers, contributes to eco-conscious IT. A notable 2020 study revealed an interesting trend: despite a 550% increase in computing output, data center energy consumption only grew by 6%. This underscores the efficiency achieved through sustainable practices in cloud computing. Ready to embrace the benefits of cloud migration? Contact Gart today, and let us guide you through a seamless transition to the cloud. The time is now to elevate your operations and embrace the future of digital efficiency. Why Green Clouds Matter for Your Business in 2026 Three forces converged in 2025-2026 to push green cloud computing from "nice to have" to a genuine business driver: Regulatory pressure: The EU Corporate Sustainability Reporting Directive (CSRD) and SEC climate disclosure rules now require enterprises to report Scope 1, 2, and 3 emissions — including cloud infrastructure usage. Enterprise buyer requirements: Procurement teams at large enterprises increasingly include carbon reporting requirements in vendor questionnaires, making sustainability data a sales prerequisite. Investor scrutiny: ESG scores directly affect access to capital and valuation multiples, particularly for Series B+ technology companies seeking institutional investment. Cost alignment: Green cloud practices — rightsizing, autoscaling, spot instances — reduce idle waste that is simultaneously bad for the environment and for your AWS bill. Key insight: Green cloud is not a separate initiative competing with cost optimization or reliability engineering. In practice, the same practices that reduce idle resource waste — autoscaling, rightsizing, efficient scheduling — also reduce carbon emissions. Sustainability and FinOps are two lenses on the same operational problem. Organizations that integrate carbon accountability into cloud governance today gain a significant competitive advantage: they satisfy regulatory requirements, win enterprise deals, and operate more efficiently — simultaneously. For more on the business case, our analysis of cloud migration's financial benefits covers the ROI picture in detail. Is Cloud Actually Greener Than On-Premises? The short answer is yes — in most cases, by a significant margin. But the specifics matter for your ESG reporting, so here is the honest breakdown. Hyperscale data centers operated by AWS, Azure, and Google Cloud run at Power Usage Effectiveness (PUE) ratios of 1.1-1.2, meaning they use only 10-20% overhead energy for cooling and infrastructure. The average enterprise data center runs at PUE 1.5-2.0, using 50-100% overhead energy on top of compute. Combined with renewable energy procurement at scale, this creates a material and measurable carbon advantage for properly architected cloud workloads. FactorTypical Enterprise Data CenterHyperscale Cloud (AWS/Azure/GCP)Power Usage Effectiveness (PUE)1.5 – 2.01.1 – 1.2Average server utilization10 – 15%65 – 80%Renewable energy shareTypically 0 – 30%100% (committed by 2025-2030)Cooling technologyCRAC units, legacy air coolingLiquid cooling, AI-driven optimizationHardware refresh cycle5-7 years (manual procurement)3-4 years (continuous efficiency gains)Carbon reduction potentialBaseline reference80-96% vs on-prem (451 Research)Water usage trackingHigh, rarely monitoredActively tracked; all providers targeting net-zero water by 2030Is Cloud Actually Greener Than On-Premises? Important caveat for ESG reporting: Cloud migration reduces your carbon footprint on average — but the actual reduction varies significantly by workload, cloud region, and modernization depth. A lift-and-shift of an oversized, poorly optimized workload achieves less than a rightsized, cloud-native deployment. Always validate reduction claims with workload-level data before publishing ESG disclosures. How to Measure Your Cloud Carbon Footprint You cannot reduce what you do not measure. Cloud carbon measurement has matured significantly in the past two years. Provider-native tools are free, require no configuration, and can be integrated into your existing observability stack in less than a day of engineering effort. Provider-Native Carbon Measurement Tools AWS AWS Customer Carbon Footprint Tool Covers Scope 1, 2, and 3 emissions from AWS service usage. Available free in the AWS Billing Console. Shows estimated emissions reduction vs on-premises. Updates monthly. Azure Emissions Impact Dashboard Available for Microsoft 365 and Azure workloads. Provides datacenter PUE and renewable energy percentage per region. Integrates with Microsoft Cloud for Sustainability platform. Google Cloud Google Cloud Carbon Footprint Displays gross carbon emissions by project, service, and region. Covers Scope 1, 2, and 3. Integrated into Google Cloud Console. Updates monthly. Cloud Carbon KPIs to Track Monthly gCO2eq per compute-hour — normalizes emissions across instance types and regions for fair comparison Carbon intensity by region — which of your regions run on a higher share of renewable energy Idle resource carbon waste — emissions attributable to over-provisioned or unused infrastructure Renewable energy percentage — share of workloads running in 100% renewable-energy cloud regions Carbon efficiency score — gCO2eq emitted per unit of business output (API calls, transactions, active users) Quick Win Enable the AWS Customer Carbon Footprint Tool today — it requires zero configuration and delivers a baseline Scope 1/2/3 report within minutes. For multi-cloud visibility, the open-source Cloud Carbon Footprint project provides unified dashboards across AWS, Azure, and GCP without any vendor lock-in. Green Cloud Strategies That Actually Reduce Emissions The following strategies are ranked by carbon reduction potential and practical implementation effort. These are the tactics we apply in client engagements at Gart — not theoretical frameworks, but operational playbooks that produce measurable, reportable results. 1 Rightsize First — Eliminate Idle Carbon Before Anything Else The average enterprise cloud environment runs at 15-25% average CPU utilization. Every idle CPU cycle is wasted compute energy. Use AWS Compute Optimizer, Azure Advisor, or GCP Recommender to identify over-provisioned instances and rightsize to actual utilization before any other green initiative. This single step typically reduces cloud carbon 20-40%. 2 Deploy to Low-Carbon Regions Cloud regions vary significantly in electricity grid carbon intensity. AWS eu-west-1 (Ireland) runs on substantially more renewable energy than us-east-1 (Northern Virginia) at certain times. For latency-tolerant workloads, region selection is often the highest-leverage carbon reduction decision you can make — with zero architectural changes required. 3 Implement Carbon-Aware Workload Scheduling Batch jobs, ML training pipelines, and data processing workloads are flexible on timing. The Green Software Foundation's Carbon Aware SDK provides real-time carbon intensity data for all major cloud regions, enabling automated scheduling of flexible workloads to run when and where the grid is greenest. 4 Use Spot and Preemptible Instances for Flexible Workloads Spot and preemptible instances run on otherwise-idle cloud capacity — consuming resources that would emit carbon regardless. For fault-tolerant workloads such as batch processing, ML training, and CI/CD pipelines, they deliver 70-90% cost savings and improve overall resource utilization efficiency across the cloud provider's fleet. 5 Containerize and Optimize with Kubernetes Container workloads achieve significantly higher server utilization than VMs. A well-tuned Kubernetes cluster running at 70%+ resource utilization emits substantially less carbon per unit of compute than a fleet of half-utilized VMs. Green Kubernetes optimization — bin packing, node autoscaling with Karpenter, and Spot node groups — is one of the highest-ROI green cloud investments. 6 Migrate to ARM/Graviton Processors AWS Graviton3, Google Tau, and Azure Ampere processors deliver equivalent performance at 40-60% lower power draw compared to traditional x86 instances. For workloads that are compatible with ARM architecture — which is the majority of modern containerized applications — this is a direct carbon and cost reduction with minimal migration effort. AWS vs Azure vs Google Cloud: Sustainability Comparison 2026 All three hyperscalers have made serious sustainability commitments — but their approaches, tools, and progress toward those commitments differ in ways that matter for teams making cloud provider decisions with ESG requirements in scope. CriterionAWSMicrosoft AzureGoogle CloudRenewable energy status100% renewable across 19 regions (reached 2023)100% renewable by 2025; carbon negative by 2030Carbon-neutral since 2007; 24/7 carbon-free by 2030Net-zero targetNet-zero Scope 1, 2 & 3 by 2040 (Climate Pledge)Remove all historical carbon by 2050Net-zero across all emissions by 2030Carbon measurement toolAWS Customer Carbon Footprint ToolEmissions Impact Dashboard; Cloud for SustainabilityGoogle Cloud Carbon Footprint (Console)Water commitmentWater Positive by 2030Water Positive by 2030; WUE published by regionReplenish 120% of water consumed by 2030Carbon-aware region dataEmerging via Sustainability Pillar guidancePublished datacenter carbon intensity dataReal-time carbon-free energy % by region in ConsoleHardware circularityAsset refurbishment and lifecycle managementCircular Centers — server repurposing; zero waste by 2030Server refurbishment; continuous chip efficiency R&DBest forOrganizations already deep in the AWS ecosystemEnterprises with Microsoft 365 and Azure AD investmentTeams prioritizing 24/7 carbon-free accuracy and data transparencyAWS vs Azure vs Google Cloud: Sustainability Comparison 2026 Google: Carbon-Free Operations, Water Conservation, and Cloud Sustainability Google aims to power all its global operations with 100% carbon-free energy around the clock by 2030. They achieved carbon-neutrality in 2007 and have been using renewable energy for their data centers since 2017. The company invests in technology for carbon removal solutions to offset its emissions. Google also has a goal to replenish 120% of the water consumed in its data centers and facilities. Public cloud services, like Google's, rely on energy-efficient hyperscale data centers. These centers outperform smaller servers thanks to innovative infrastructure design and advanced cooling tech. Operating in a Google data center reduces electricity needs for IT hardware, leading to higher power usage effectiveness (PUE) compared to typical enterprise data centers. Google Cloud not only prioritizes sustainability in its operations but also offers the Carbon Footprint tool for customers. This tool allows users to monitor and measure carbon emissions from their cloud applications, covering Scope 1, 2, and 3. It serves as an emissions calculator, aiding companies in reporting their gross carbon footprint and offering best practices for building low-carbon applications in Google Cloud. Read more: Google Cloud Migration Services Microsoft: Pioneering Carbon Reduction, Circular Solutions, and Cloud Sustainability Microsoft aims to cut carbon emissions by over 50% by 2030 and eliminate its historical carbon footprint by 2050. They're shifting to 100% renewable energy for data centers and buildings by 2025, and zero waste is on the agenda by 2030. Circular Centers repurpose old servers to combat growing e-waste, introduced as part of Microsoft's sustainability strategy since 2020. Tools like Microsoft Cloud for Sustainability offer real-time insights into carbon emissions, while the Emissions Impact Dashboard for Microsoft 365 calculates cloud workload footprints. Microsoft's focus areas include lowering energy consumption, green data centers, water management, and waste reduction through responsible sourcing and recycling. Four key drivers reduce the energy and carbon footprint of the Microsoft Cloud: IT operational efficiency, equipment efficiency, datacenter infrastructure efficiency, and new renewable electricity, targeting 100% by 2025. Read more: Azure Migration Services Amazon: Leading the Charge with Net-Zero Commitment and Sustainable Solutions As a co-founder of The Climate Pledge, Amazon joins 400 global companies committed to achieving net-zero carbon emissions by 2040. Their strategies include reducing material usage, innovating for energy efficiency, and embracing renewable energy solutions. Amazon, the largest corporate buyer of renewable energy since 2020, leads in sustainable practices to decarbonize its transportation network. A study by 451 Research found that US enterprises, on average, could cut their carbon footprint by up to 88% by moving to AWS from on-premises data centers. Amazon introduces the AWS Customer Carbon Footprint Tool, an emissions calculator for customers. It provides data on carbon footprint, including Scope 1 and Scope 2 emissions from cloud service usage. It also estimates the carbon emission reduction achieved by transitioning operations to the cloud. Read more: AWS Migration Services For deeper guidance on migrating to each provider, see: AWS Migration Services · Azure Migration Services · Google Cloud Migration Services GreenOps: Embedding Sustainability into Cloud Operations GreenOps is the operational discipline of tracking and reducing cloud carbon alongside cost and reliability — treating gCO2eq as a first-class engineering metric, not an afterthought in an annual sustainability report. The Cloud Native Computing Foundation (CNCF) Environmental Sustainability TAG provides open standards and tooling for teams implementing GreenOps at scale. Green DevOps Practices with Measurable Carbon Impact DevOps PracticeCarbon Reduction MechanismTypical ImpactKubernetes node autoscalingEliminates idle node capacity during low-traffic periods30-60% reduction in baseline compute emissionsEnvironment scheduling (dev/test)Auto-shutdown non-prod environments at nights and weekendsUp to 65% reduction in dev/test carbon wasteInfrastructure as Code (IaC)Eliminates configuration drift and over-provisioning at deployment15-30% reduction in provisioning wasteContainer image optimizationSmaller images — faster cold starts, less idle compute during scale events10-25% reduction in container runtime emissionsGraviton/ARM instance migrationARM processors deliver equivalent performance at 40% lower power drawUp to 40% reduction in compute-related emissionsCI/CD pipeline efficiencyParallel testing, caching, and artifact optimization reduce build infrastructure carbon20-40% reduction in CI/CD emissionsGreen DevOps Practices with Measurable Carbon Impact "In every cloud environment we audit, the single largest source of wasted carbon is the same as the largest source of wasted cost: idle and over-provisioned resources. Rightsizing is not a sustainability project — it is good engineering. We just need to start measuring it in both dollars and grams of CO2."— Fedir Kompaniiets, Co-founder & DevOps Expert, Gart Solutions FinOps and Sustainability: Two Goals, One Strategy The FinOps Foundation added sustainability as a formal pillar of the FinOps framework in 2024, recognizing that carbon optimization and cost optimization share the same root causes. The table below maps FinOps practices to their direct carbon impact — making the case for treating these as a unified program rather than parallel initiatives: FinOps PracticeCost ImpactCarbon ImpactRightsizing instances15-40% compute cost reductionProportional reduction in Scope 2 emissionsSpot / preemptible instances70-90% discount vs on-demandImproves fleet utilization = lower per-unit carbonResource tagging and cost allocation20-35% waste reduction over 12 monthsEnables carbon-by-team visibility and accountabilityScheduled dev/test shutdownUp to 65% dev/test environment savingsDirect elimination of idle compute carbonStorage lifecycle policies40-95% storage cost reductionReduces data center storage hardware demandGraviton/ARM migration20-30% compute cost savings40% reduction in processor-level power drawFinOps and Sustainability: Two Goals, One Strategy Our AWS cost optimization guide covers the tactical implementation of these FinOps practices in detail, with concrete savings estimates for each technique. How AI Workloads Affect Cloud Carbon Emissions AI workloads represent one of the fastest-growing sources of cloud carbon emissions. Training a large foundation model can emit hundreds of tonnes of CO2 — comparable to the lifetime emissions of multiple vehicles. Inference workloads are more manageable but accumulate significantly at scale. Engineering leaders need a deliberate strategy for AI's cloud carbon footprint before it becomes a material ESG reporting problem. Train in carbon-light regions: Google Cloud publishes real-time carbon-free energy percentages by region — use this data to schedule GPU training jobs dynamically rather than defaulting to the nearest or cheapest region. Use spot and preemptible GPU instances: Large training runs on spot GPU instances (P3, A100, H100) reduce both cost and carbon intensity per training step by 70-90% for fault-tolerant workloads. Apply quantization and distillation: Reducing model precision (INT8, INT4) and distilling large models to smaller task-specific versions reduces inference compute requirements by 4-10x with minimal accuracy loss for most production use cases. Cache inference results semantically: For repetitive queries — chatbots, search, recommendations — semantic caching reduces redundant inference compute by 30-60%, with direct carbon and cost benefit. Carbon-aware training scheduling: The Green Software Foundation's Carbon Aware SDK enables automatic scheduling of training runs during hours of peak renewable availability in your target region. Gart Case Study: 32% Cloud Carbon Reduction for a SaaS Platform Case Study · SaaS · AWS Green Cloud Optimization for a European B2B SaaS Platform A 120-person SaaS company running on AWS eu-west-1 engaged Gart Solutions after receiving ESG questionnaires from three enterprise clients requiring documented Scope 3 emissions reporting. Their infrastructure was running at 18% average CPU utilization across a fleet of on-demand EC2 instances — a common pattern in organizations that grew fast and never stopped to right-size. 32% Reduction in cloud carbon emissions over 6 months 38% Infrastructure cost reduction over the same period 71% Avg. cluster utilization (up from 18% on EC2) What we did: Migrated from on-demand EC2 to a Kubernetes cluster on Graviton3 instances with Karpenter node autoscaling, moved all batch processing to Spot instances, implemented automated dev/test environment shutdown on weeknights and weekends, migrated ML inference endpoints to AWS Lambda, and established monthly carbon reporting via the AWS Customer Carbon Footprint Tool tied to engineering OKRs. Total engineering effort: 11 weeks, zero production downtime. Sustainable Cloud Architecture: A Practical Framework The AWS Well-Architected Sustainability Pillar and the Green Software Foundation's Software Carbon Intensity (SCI) specification together provide a consistent, auditable framework for sustainability assessments. We apply both in client engagements to ensure recommendations are grounded in recognized industry standards. Understand your impact: Establish a carbon baseline using provider tools before any optimization work. You need a measurable starting point to demonstrate reduction progress in ESG reports. Set sustainability goals tied to engineering KPIs: A carbon reduction target (e.g., 30% reduction in 12 months) becomes actionable when it is expressed as gCO2eq per transaction — something engineering teams can directly influence. Maximize utilization: Drive instance, cluster, and function utilization as high as reliability constraints allow. Idle capacity is the primary source of avoidable cloud carbon. Adopt more efficient offerings continuously: Graviton3, serverless, and managed container services consistently deliver better performance-per-watt than their predecessors. Build adoption into your standard upgrade cycle. Use managed services strategically: AWS RDS, EKS, and serverless functions are operated at higher efficiency than self-managed equivalents. The carbon overhead of management tooling is absorbed by the provider's scale. Reduce downstream impact: Optimize API payloads, image sizes, and content delivery architecture to reduce the energy consumed by clients and CDN layers accessing your services. Conceptual Frameworks for Green Clouds There are several frameworks that provide a structured roadmap for sustainable cloud computing: Ecological Modernization Theory Triple Bottom Line (TBL) Life Cycle Assessment (LCA) Ecological Modernization Theory Ecological Modernization Theory (EMT) emphasizes that technological advancement, rather than being a threat to the environment, can align with ecological objectives. The framework promotes leveraging innovation to minimize environmental impact while maintaining or enhancing efficiency. In cloud infrastructures, this theory supports the integration of eco-friendly practices such as: Adoption of energy-efficient hardware. Investment in advanced cooling systems. Use of renewable energy sources for powering data centers. Cloud service providers can modernize their operations to reduce energy consumption and carbon footprints while maintaining service quality and scalability. Triple Bottom Line (TBL) The TBL framework evaluates sustainability across three dimensions: economic, social, and environmental. In the context of cloud computing, it offers a balanced perspective to achieve sustainability goals: Economic Dimension: Ensures the financial viability of sustainable practices, such as reducing operational costs through energy-efficient technologies. Social Dimension: Encourages corporate social responsibility by promoting awareness and equitable practices in communities where data centers operate. Environmental Dimension: Prioritizes minimizing the ecological footprint through renewable energy integration, efficient resource usage, and e-waste management. The TBL approach promotes a holistic view, ensuring that economic growth in the cloud industry does not come at the expense of environmental or social well-being. Life Cycle Assessment (LCA) LCA examines the environmental impact of cloud computing across its entire lifecycle, from raw material extraction to disposal. This detailed analysis helps identify the stages where intervention is most needed: Stages in LCA: Raw Material Extraction: Assessing the environmental costs of producing hardware components. Manufacturing: Evaluating emissions and resource use during production. Deployment and Operation: Measuring energy and water consumption during active use. End-of-Life Management: Analyzing the ecological impact of decommissioning and recycling infrastructure components. By understanding these stages, cloud providers can implement targeted strategies to mitigate the environmental impact, such as sourcing sustainable materials and adopting energy-efficient operations. Empower Your Green Transition Ready to take the leap into the public cloud? Before you dive in, a word of advice: Cloud migration is more than a simple "lift and shift." It requires a strategic approach, choosing the right vendor, ensuring infrastructure readiness, and aligning IT and business objectives. However, the investment in this transition pays off. Shifting operations to the public cloud and prioritizing cloud-based applications can potentially reduce global emissions and energy consumption by up to 20 percent. Feeling inspired to make a positive impact? Now's the time to act. Contact Gart, and we'll guide you through the migration process. Let's contribute to a greener future together! Gart Solutions · Cloud & DevOps Consulting Ready to Make Your Cloud Infrastructure Measurably Greener? We help engineering teams in Europe and North America reduce cloud carbon footprint and infrastructure costs simultaneously — through rightsizing, green Kubernetes optimization, FinOps integration, and ESG-ready carbon reporting that satisfies enterprise and investor requirements. Cloud Migration Green Kubernetes FinOps & Carbon Reporting GreenOps Audit DevOps Services Infrastructure as Code Book a Free Cloud Sustainability Assessment → Explore Cloud Services ⭐ 4.9/5 on Clutch (15 reviews) 🏆 50+ cloud migrations delivered 🌍 EU & North America clients ✅ AWS & Azure certified architects Fedir Kompaniiets Co-founder & CEO, Gart Solutions · Cloud Architect & DevOps Consultant Fedir is a technology enthusiast with over a decade of diverse industry experience. He co-founded Gart Solutions to address complex tech challenges related to Digital Transformation, helping businesses focus on what matters most — scaling. Fedir is committed to driving sustainable IT transformation, helping SMBs innovate, plan future growth, and navigate the "tech madness" through expert DevOps and Cloud managed services. Connect on LinkedIn.

In my experience optimizing cloud costs, especially on AWS, I often find that many quick wins are in the "easy to implement - good savings potential" quadrant. [lwptoc] That's why I've decided to share some straightforward methods for optimizing expenses on AWS that will help you save over 80% of your budget. Choose reserved instances Potential Savings: Up to 72% Choosing reserved instances involves committing to a subscription, even partially, and offers a discount for long-term rentals of one to three years. While planning for a year is often deemed long-term for many companies, especially in Ukraine, reserving resources for 1-3 years carries risks but comes with the reward of a maximum discount of up to 72%. You can check all the current pricing details on the official website - Amazon EC2 Reserved Instances Purchase Saving Plans (Instead of On-Demand) Potential Savings: Up to 72% There are three types of saving plans: Compute Savings Plan, EC2 Instance Savings Plan, SageMaker Savings Plan. AWS Compute Savings Plan is an Amazon Web Services option that allows users to receive discounts on computational resources in exchange for committing to using a specific volume of resources over a defined period (usually one or three years). This plan offers flexibility in utilizing various computing services, such as EC2, Fargate, and Lambda, at reduced prices. AWS EC2 Instance Savings Plan is a program from Amazon Web Services that offers discounted rates exclusively for the use of EC2 instances. This plan is specifically tailored for the utilization of EC2 instances, providing discounts for a specific instance family, regardless of the region. AWS SageMaker Savings Plan allows users to get discounts on SageMaker usage in exchange for committing to using a specific volume of computational resources over a defined period (usually one or three years). The discount is available for one and three years with the option of full, partial upfront payment, or no upfront payment. EC2 can help save up to 72%, but it applies exclusively to EC2 instances. Utilize Various Storage Classes for S3 (Including Intelligent Tier) Potential Savings: 40% to 95% AWS offers numerous options for storing data at different access levels. For instance, S3 Intelligent-Tiering automatically stores objects at three access levels: one tier optimized for frequent access, 40% cheaper tier optimized for infrequent access, and 68% cheaper tier optimized for rarely accessed data (e.g., archives). S3 Intelligent-Tiering has the same price per 1 GB as S3 Standard — $0.023 USD. However, the key advantage of Intelligent Tiering is its ability to automatically move objects that haven't been accessed for a specific period to lower access tiers. Every 30, 90, and 180 days, Intelligent Tiering automatically shifts an object to the next access tier, potentially saving companies from 40% to 95%. This means that for certain objects (e.g., archives), it may be appropriate to pay only $0.0125 USD per 1 GB or $0.004 per 1 GB compared to the standard price of $0.023 USD. Information regarding the pricing of Amazon S3 AWS Compute Optimizer Potential Savings: quite significant The AWS Compute Optimizer dashboard is a tool that lets users assess and prioritize optimization opportunities for their AWS resources. The dashboard provides detailed information about potential cost savings and performance improvements, as the recommendations are based on an analysis of resource specifications and usage metrics. The dashboard covers various types of resources, such as EC2 instances, Auto Scaling groups, Lambda functions, Amazon ECS services on Fargate, and Amazon EBS volumes. For example, AWS Compute Optimizer reproduces information about underutilized or overutilized resources allocated for ECS Fargate services or Lambda functions. Regularly keeping an eye on this dashboard can help you make informed decisions to optimize costs and enhance performance. Use Fargate in EKS for underutilized EC2 nodes If your EKS nodes aren't fully used most of the time, it makes sense to consider using Fargate profiles. With AWS Fargate, you pay for a specific amount of memory/CPU resources needed for your POD, rather than paying for an entire EC2 virtual machine. For example, let's say you have an application deployed in a Kubernetes cluster managed by Amazon EKS (Elastic Kubernetes Service). The application experiences variable traffic, with peak loads during specific hours of the day or week (like a marketplace or an online store), and you want to optimize infrastructure costs. To address this, you need to create a Fargate Profile that defines which PODs should run on Fargate. Configure Kubernetes Horizontal Pod Autoscaler (HPA) to automatically scale the number of POD replicas based on their resource usage (such as CPU or memory usage). Manage Workload Across Different Regions Potential Savings: significant in most cases When handling workload across multiple regions, it's crucial to consider various aspects such as cost allocation tags, budgets, notifications, and data remediation. Cost Allocation Tags: Classify and track expenses based on different labels like program, environment, team, or project. AWS Budgets: Define spending thresholds and receive notifications when expenses exceed set limits. Create budgets specifically for your workload or allocate budgets to specific services or cost allocation tags. Notifications: Set up alerts when expenses approach or surpass predefined thresholds. Timely notifications help take actions to optimize costs and prevent overspending. Remediation: Implement mechanisms to rectify expenses based on your workload requirements. This may involve automated actions or manual interventions to address cost-related issues. Regional Variances: Consider regional differences in pricing and data transfer costs when designing workload architectures. Reserved Instances and Savings Plans: Utilize reserved instances or savings plans to achieve cost savings. AWS Cost Explorer: Use this tool for visualizing and analyzing your expenses. Cost Explorer provides insights into your usage and spending trends, enabling you to identify areas of high costs and potential opportunities for cost savings. Transition to Graviton (ARM) Potential Savings: Up to 30% Graviton utilizes Amazon's server-grade ARM processors developed in-house. The new processors and instances prove beneficial for various applications, including high-performance computing, batch processing, electronic design automation (EDA) automation, multimedia encoding, scientific modeling, distributed analytics, and machine learning inference on processor-based systems. The processor family is based on ARM architecture, likely functioning as a system on a chip (SoC). This translates to lower power consumption costs while still offering satisfactory performance for the majority of clients. Key advantages of AWS Graviton include cost reduction, low latency, improved scalability, enhanced availability, and security. Spot Instances Instead of On-Demand Potential Savings: Up to 30% Utilizing spot instances is essentially a resource exchange. When Amazon has surplus resources lying idle, you can set the maximum price you're willing to pay for them. The catch is that if there are no available resources, your requested capacity won't be granted. However, there's a risk that if demand suddenly surges and the spot price exceeds your set maximum price, your spot instance will be terminated. Spot instances operate like an auction, so the price is not fixed. We specify the maximum we're willing to pay, and AWS determines who gets the computational power. If we are willing to pay $0.1 per hour and the market price is $0.05, we will pay exactly $0.05. Use Interface Endpoints or Gateway Endpoints to save on traffic costs (S3, SQS, DynamoDB, etc.) Potential Savings: Depends on the workload Interface Endpoints operate based on AWS PrivateLink, allowing access to AWS services through a private network connection without going through the internet. By using Interface Endpoints, you can save on data transfer costs associated with traffic. Utilizing Interface Endpoints or Gateway Endpoints can indeed help save on traffic costs when accessing services like Amazon S3, Amazon SQS, and Amazon DynamoDB from your Amazon Virtual Private Cloud (VPC). Key points: Amazon S3: With an Interface Endpoint for S3, you can privately access S3 buckets without incurring data transfer costs between your VPC and S3. Amazon SQS: Interface Endpoints for SQS enable secure interaction with SQS queues within your VPC, avoiding data transfer costs for communication with SQS. Amazon DynamoDB: Using an Interface Endpoint for DynamoDB, you can access DynamoDB tables in your VPC without incurring data transfer costs. Additionally, Interface Endpoints allow private access to AWS services using private IP addresses within your VPC, eliminating the need for internet gateway traffic. This helps eliminate data transfer costs for accessing services like S3, SQS, and DynamoDB from your VPC. Optimize Image Sizes for Faster Loading Potential Savings: Depends on the workload Optimizing image sizes can help you save in various ways. Reduce ECR Costs: By storing smaller instances, you can cut down expenses on Amazon Elastic Container Registry (ECR). Minimize EBS Volumes on EKS Nodes: Keeping smaller volumes on Amazon Elastic Kubernetes Service (EKS) nodes helps in cost reduction. Accelerate Container Launch Times: Faster container launch times ultimately lead to quicker task execution. Optimization Methods: Use the Right Image: Employ the most efficient image for your task; for instance, Alpine may be sufficient in certain scenarios. Remove Unnecessary Data: Trim excess data and packages from the image. Multi-Stage Image Builds: Utilize multi-stage image builds by employing multiple FROM instructions. Use .dockerignore: Prevent the addition of unnecessary files by employing a .dockerignore file. Reduce Instruction Count: Minimize the number of instructions, as each instruction adds extra weight to the hash. Group instructions using the && operator. Layer Consolidation: Move frequently changing layers to the end of the Dockerfile. These optimization methods can contribute to faster image loading, reduced storage costs, and improved overall performance in containerized environments. Use Load Balancers to Save on IP Address Costs Potential Savings: depends on the workload Starting from February 2024, Amazon begins billing for each public IPv4 address. Employing a load balancer can help save on IP address costs by using a shared IP address, multiplexing traffic between ports, load balancing algorithms, and handling SSL/TLS. By consolidating multiple services and instances under a single IP address, you can achieve cost savings while effectively managing incoming traffic. Optimize Database Services for Higher Performance (MySQL, PostgreSQL, etc.) Potential Savings: depends on the workload AWS provides default settings for databases that are suitable for average workloads. If a significant portion of your monthly bill is related to AWS RDS, it's worth paying attention to parameter settings related to databases. Some of the most effective settings may include: Use Database-Optimized Instances: For example, instances in the R5 or X1 class are optimized for working with databases. Choose Storage Type: General Purpose SSD (gp2) is typically cheaper than Provisioned IOPS SSD (io1/io2). AWS RDS Auto Scaling: Automatically increase or decrease storage size based on demand. If you can optimize the database workload, it may allow you to use smaller instance sizes without compromising performance. Regularly Update Instances for Better Performance and Lower Costs Potential Savings: Minor As Amazon deploys new servers in their data processing centers to provide resources for running more instances for customers, these new servers come with the latest equipment, typically better than previous generations. Usually, the latest two to three generations are available. Make sure you update regularly to effectively utilize these resources. Take Memory Optimize instances, for example, and compare the price change based on the relevance of one instance over another. Regular updates can ensure that you are using resources efficiently. InstanceGenerationDescriptionOn-Demand Price (USD/hour)m6g.large6thInstances based on ARM processors offer improved performance and energy efficiency.$0.077m5.large5thGeneral-purpose instances with a balanced combination of CPU and memory, designed to support high-speed network access.$0.096m4.large4thA good balance between CPU, memory, and network resources.$0.1m3.large3rdOne of the previous generations, less efficient than m5 and m4.Not avilable Use RDS Proxy to reduce the load on RDS Potential for savings: Low RDS Proxy is used to relieve the load on servers and RDS databases by reusing existing connections instead of creating new ones. Additionally, RDS Proxy improves failover during the switch of a standby read replica node to the master. Imagine you have a web application that uses Amazon RDS to manage the database. This application experiences variable traffic intensity, and during peak periods, such as advertising campaigns or special events, it undergoes high database load due to a large number of simultaneous requests. During peak loads, the RDS database may encounter performance and availability issues due to the high number of concurrent connections and queries. This can lead to delays in responses or even service unavailability. RDS Proxy manages connection pools to the database, significantly reducing the number of direct connections to the database itself. By efficiently managing connections, RDS Proxy provides higher availability and stability, especially during peak periods. Using RDS Proxy reduces the load on RDS, and consequently, the costs are reduced too. Define the storage policy in CloudWatch Potential for savings: depends on the workload, could be significant. The storage policy in Amazon CloudWatch determines how long data should be retained in CloudWatch Logs before it is automatically deleted. Setting the right storage policy is crucial for efficient data management and cost optimization. While the "Never" option is available, it is generally not recommended for most use cases due to potential costs and data management issues. Typically, best practice involves defining a specific retention period based on your organization's requirements, compliance policies, and needs. Avoid using an undefined data retention period unless there is a specific reason. By doing this, you are already saving on costs. Configure AWS Config to monitor only the events you need Potential for savings: depends on the workload AWS Config allows you to track and record changes to AWS resources, helping you maintain compliance, security, and governance. AWS Config provides compliance reports based on rules you define. You can access these reports on the AWS Config dashboard to see the status of tracked resources. You can set up Amazon SNS notifications to receive alerts when AWS Config detects non-compliance with your defined rules. This can help you take immediate action to address the issue. By configuring AWS Config with specific rules and resources you need to monitor, you can efficiently manage your AWS environment, maintain compliance requirements, and avoid paying for rules you don't need. Use lifecycle policies for S3 and ECR Potential for savings: depends on the workload S3 allows you to configure automatic deletion of individual objects or groups of objects based on specified conditions and schedules. You can set up lifecycle policies for objects in each specific bucket. By creating data migration policies using S3 Lifecycle, you can define the lifecycle of your object and reduce storage costs. These object migration policies can be identified by storage periods. You can specify a policy for the entire S3 bucket or for specific prefixes. The cost of data migration during the lifecycle is determined by the cost of transfers. By configuring a lifecycle policy for ECR, you can avoid unnecessary expenses on storing Docker images that you no longer need. Switch to using GP3 storage type for EBS Potential for savings: 20% By default, AWS creates gp2 EBS volumes, but it's almost always preferable to choose gp3 — the latest generation of EBS volumes, which provides more IOPS by default and is cheaper. For example, in the US-east-1 region, the price for a gp2 volume is $0.10 per gigabyte-month of provisioned storage, while for gp3, it's $0.08/GB per month. If you have 5 TB of EBS volume on your account, you can save $100 per month by simply switching from gp2 to gp3. Switch the format of public IP addresses from IPv4 to IPv6 Potential for savings: depending on the workload Starting from February 1, 2024, AWS will begin charging for each public IPv4 address at a rate of $0.005 per IP address per hour. For example, taking 100 public IP addresses on EC2 x $0.005 per public IP address per month x 730 hours = $365.00 per month. While this figure might not seem huge (without tying it to the company's capabilities), it can add up to significant network costs. Thus, the optimal time to transition to IPv6 was a couple of years ago or now. Here are some resources about this recent update that will guide you on how to use IPv6 with widely-used services — AWS Public IPv4 Address Charge. Collaborate with AWS professionals and partners for expertise and discounts Potential for savings: ~5% of the contract amount through discounts. AWS Partner Network (APN) Discounts: Companies that are members of the AWS Partner Network (APN) can access special discounts, which they can pass on to their clients. Partners reaching a certain level in the APN program often have access to better pricing offers. Custom Pricing Agreements: Some AWS partners may have the opportunity to negotiate special pricing agreements with AWS, enabling them to offer unique discounts to their clients. This can be particularly relevant for companies involved in consulting or system integration. Reseller Discounts: As resellers of AWS services, partners can purchase services at wholesale prices and sell them to clients with a markup, still offering a discount from standard AWS prices. They may also provide bundled offerings that include AWS services and their own additional services. Credit Programs: AWS frequently offers credit programs or vouchers that partners can pass on to their clients. These could be promo codes or discounts for a specific period. Seek assistance from AWS professionals and partners. Often, this is more cost-effective than purchasing and configuring everything independently. Given the intricacies of cloud space optimization, expertise in this matter can save you tens or hundreds of thousands of dollars. More valuable tips for optimizing costs and improving efficiency in AWS environments: Scheduled TurnOff/TurnOn for NonProd environments: If the Development team is in the same timezone, significant savings can be achieved by, for example, scaling the AutoScaling group of instances/clusters/RDS to zero during the night and weekends when services are not actively used. Move static content to an S3 Bucket & CloudFront: To prevent service charges for static content, consider utilizing Amazon S3 for storing static files and CloudFront for content delivery. Use API Gateway/Lambda/Lambda Edge where possible: In such setups, you only pay for the actual usage of the service. This is especially noticeable in NonProd environments where resources are often underutilized. If your CI/CD agents are on EC2, migrate to CodeBuild: AWS CodeBuild can be a more cost-effective and scalable solution for your continuous integration and delivery needs. CloudWatch covers the needs of 99% of projects for Monitoring and Logging: Avoid using third-party solutions if AWS CloudWatch meets your requirements. It provides comprehensive monitoring and logging capabilities for most projects. Feel free to reach out to me or other specialists for an audit, a comprehensive optimization package, or just advice.