AWS Cost Optimization: Top 10 Strategies, Best Practices & Real Case Overview

On-prem to cloud migration has stopped being a one-time IT initiative and become something enterprises revisit every time their infrastructure, compliance posture, or cost base shifts. If you are a CTO or CIO scoping a move to AWS, the question by 2026 is rarely whether the cloud belongs in your architecture — most environments already touch it in some form. The harder questions are which workloads to move first, which migration path fits each one, and how to keep the original business case intact once the AWS invoices start arriving. [lwptoc] This guide breaks down what is actually driving on-premise to AWS cloud migration today, how the AWS Migration Acceleration Program (MAP) can offset part of the cost, and how to choose between rehosting, replatforming, and refactoring without over-engineering a project that simply needs to ship. Our AWS cloud migration services team has run this exact playbook for healthcare, fintech, and e-commerce platforms retiring legacy data centers — the recommendations below come from those engagements, not just vendor documentation. Why On-Prem to Cloud Migration Is Still on Every CIO’s Agenda in 2026 It would be easy to assume cloud migration is a solved problem by now. It is not. Worldwide IT spending continues climbing toward several trillion dollars a year, and a growing share of that budget is shifting away from owned data center hardware toward cloud infrastructure, AI-optimized compute, and managed services. For most enterprises still running meaningful workloads on-premise, that shift shows up as an annual conversation: a hardware refresh that is overdue, a data center lease that is expiring, or a compliance audit that flags single points of failure no cloud-native architecture would tolerate. What has changed since the early “lift everything to the cloud” era is the level of rigor expected before a migration starts. Some of the same organizations that migrated quickly between 2018 and 2022 are now re-evaluating workload placement — which is exactly why later in this guide we cover when on-prem to cloud migration should be partial rather than total. The conversation in 2026 is less about cloud versus on-premise and more about which workload belongs where, for how long, and at what cost. What’s Driving On-Premise to AWS Cloud Migration Right Now The reasons enterprises move infrastructure to AWS have not changed dramatically in substance, but their urgency has: Hardware and data center lease cycles ending. Refreshing aging servers and storage arrays on-premise often costs more, and takes longer to provision, than standing up equivalent capacity on AWS. AI and data-intensive workloads. Training and inference need elastic access to GPU capacity that most on-premise environments simply cannot provision on demand. Security and compliance modernization. AWS’s native logging, encryption, and identity tooling frequently closes audit gaps faster than building the equivalent in-house. Mergers, acquisitions, and consolidation. Combining two infrastructures is usually easier in the cloud than reconciling two physical data centers. Talent availability. Hiring and retaining engineers who specialize in legacy on-premise hardware has become harder than hiring AWS-skilled DevOps talent. Each of these drivers points toward the same conclusion: migration decisions are made workload by workload against a real total cost of ownership model, not as a single enterprise-wide mandate handed down once and left unexamined. On-Premise vs. AWS: What Actually Changes in Your Cost Structure Comparing a physical server to an EC2 instance on a CPU-and-RAM basis alone is a common but misleading exercise. It leaves out electricity, facility costs, the salaries of staff who manage racks and patch firmware, and the opportunity cost of capital tied up in hardware that starts depreciating the moment it is installed. The table below outlines what typically changes once ongoing infrastructure management moves from an on-premise model to AWS. DimensionOn-PremiseAWS After MigrationCapital modelUpfront CapEx, multi-year depreciationPay-as-you-go OpEx, scales with usageProvisioning new capacityWeeks to months (procurement, install)Minutes to hoursHardware refreshEvery 3–5 years, full re-investmentContinuous, handled by AWSDisaster recoveryRequires a second physical siteBuilt-in multi-AZ and multi-region optionsSecurity patchingManual, staff-dependentLargely automated under the shared responsibility modelWhere staff time goesRacking, cabling, firmware, facilitiesArchitecture, automation, cost governance According to AWS’s published Migration Acceleration Program customer data, organizations migrating legacy on-premise workloads to AWS save an average of 31% on infrastructure costs, run IT operations roughly 62% more efficiently, and see a 69% reduction in unplanned downtime compared with their previous on-premise environment, based on figures from the AWS Migration Acceleration Program. Those numbers will not transfer one-to-one to every environment, but they are a reasonable planning baseline once a workload has actually been re-architected rather than simply relocated. The cost governance gap. These figures only hold if cost governance keeps pace with the migration. Without a deliberate practice in place, early cloud savings commonly erode within 12–18 months as teams provision freely and nobody owns the bill. This is the gap the FinOps Foundation was built to close — it is worth standing up a lightweight FinOps practice (tagging, budgets, regular rightsizing) before, not after, your AWS spend scales. AWS Migration Acceleration Program (MAP): Funding and De-Risking the Move For organizations migrating more than a handful of servers, AWS’s Migration Acceleration Program is worth scoping early, because it changes both the cost and the structure of the project. MAP runs through three phases: Assess An AWS Partner runs a structured inventory of your current environment and produces a Migration Readiness Assessment alongside a total cost of ownership model comparing on-premise and AWS run rates. This is also the point where an infrastructure and compliance audit pays for itself, since gaps found here are far cheaper to fix before migration than after. Assessment typically runs three to eight weeks depending on environment complexity. Mobilize A test environment is built against the proposed architecture so the assumptions from the assessment phase get validated against real workloads before anything production-critical moves. Migrate and Modernize Production workloads move in planned waves, and the environment is tuned and optimized after the move rather than left exactly as it landed. MAP also provides funding that offsets part of the migration cost, typically structured as credits against your first year of AWS spend once specific milestones in your migration plan are met. The exact amount depends on workload volume and your AWS Partner’s proposal, so it is worth treating as a negotiation point during scoping rather than assuming a fixed number. Choosing Your On-Prem to Cloud Migration Strategy: The 7 Rs Early cloud migration guidance — including the original version of this article — described three migration approaches: lift-and-shift, replatforming, and refactoring. AWS’s current methodology breaks this into seven distinct strategies, and the most important addition is that two of them, Retire and Retain, explicitly acknowledge that not every workload should move at all. StrategyWhat It MeansWhen to Use ItRetireDecommission the workload entirelyApp is redundant, unused, or replaced by another systemRetainKeep it on-premise for nowCompliance requirements, recent hardware investment, or unresolved dependenciesRelocateMove infrastructure as-is to cloud-hosted virtualizationLarge VMware estates needing a fast exit from a data centerRehostLift-and-shift onto AWS infrastructureTight timelines; minimal application changes toleratedReplatformSwap select components for managed AWS servicesWant quick wins — e.g. moving a self-managed database to RDS — without a full rewriteRepurchaseMove to a different product, often SaaSA strong SaaS equivalent already exists for legacy softwareRefactorRe-architect for cloud-native patternsWorkload needs significant scalability or is core to the roadmapChoosing Your On-Prem to Cloud Migration Strategy: The 7 Rs Refactoring delivers the largest long-term efficiency gains, but it is also the most expensive and time-consuming path, so it should be reserved for workloads where the business case clearly justifies it. It typically involves breaking a monolithic application into a microservices architecture running on containers, paired with the DevOps and CI/CD practices needed to ship changes safely at that pace. Container orchestration on Kubernetes has become close to a default choice for this kind of refactor: the Cloud Native Computing Foundation’s most recent annual survey found that 82% of container users now run Kubernetes in production, including for AI inference workloads, which makes it a reasonably low-risk default rather than an experimental one. Hybrid by Design: Why Not Every Workload Should Move to the Cloud It would be incomplete to write a 2026 migration guide without addressing the other direction some workloads are moving. A meaningful share of CIOs report plans to move at least some workloads from public cloud back to private infrastructure or on-premise environments, usually for predictable, steady-state workloads where pay-as-you-go pricing stops being an advantage and starts being a cost risk. Gartner has forecast that 40% of enterprises will run hybrid compute architectures for mission-critical workloads by the end of 2026, up from roughly 8% a few years earlier, which suggests hybrid is becoming the default architecture rather than a stopgap, according to Gartner’s research. None of this contradicts the case for on-prem to cloud migration; it sharpens it. The mistake is treating migration as all-or-nothing. A steady, predictable batch-processing job that has run unchanged for five years is a poor refactor candidate, and possibly a poor migration candidate at all — that is exactly what the Retain strategy in the 7 Rs framework above is for. Meanwhile, a customer-facing application with unpredictable traffic, or a new AI workload that needs to scale up and down within hours, is exactly the kind of workload AWS is built for. Running this analysis honestly, workload by workload, against your own trade-offs between cloud and on-premises infrastructure, produces a better outcome than chasing a single migration target for the whole estate. Whichever environment a workload lands in, it still needs the same site reliability and disaster recovery planning behind it. A Practical On-Prem to Cloud Migration Roadmap Stripped of vendor framework names, a well-run on-prem to cloud migration follows roughly the same sequence regardless of which AWS program funds it: Inventory and assess. Document every workload, its dependencies, and its licensing terms before deciding anything. This is the point to run an infrastructure and compliance audit if you have not already. Build the business case. Model the on-premise run rate against the projected AWS run rate, including any MAP funding you are eligible for — not just sticker-price compute costs. Assign a 7 Rs strategy per workload. Resist the urge to apply one strategy to everything; most enterprise estates end up with a mix of rehost, replatform, and a handful of refactor candidates. Stand up the landing zone and pilot. Build the governance, networking, and identity foundation once, then validate it with a low-risk pilot before anything customer-facing moves. The landing zone and infrastructure-as-code approach you choose here shapes every workload that follows. Migrate in waves, validate, then optimize. Move workloads in planned batches, validate each one against the original business case, and put cloud cost optimization practices in place before the environment grows large enough for waste to hide. Embracing Cloud Solutions for Resource Optimization Not too long ago, the concept of "cloud services" was novel and unfamiliar to the majority of companies. Businesses were accustomed to relying on their own infrastructure, considering it sufficiently reliable and secure. However, they encountered issues that were either extremely challenging or practically unsolvable within their local data centers. The primary problem was the fluctuating availability of computing resources, with the occasional excess or shortage. Accurately estimating the required resources necessitated lengthy planning, and various types of businesses faced periods of significantly increased service load throughout the year. For example, take any well-known online store. Each new promotion, marketing campaign, or product discount triggered a substantial influx of users, putting considerable strain on the servers running the platform. This presented two core challenges: first, rapidly scaling the service to handle the increased load, and second, dealing with resource constraints when physical resources were insufficient. Creating service copies and employing load balancers proved to be more efficient and feasible with a microservices architecture.  Nonetheless, addressing the resource scarcity issue was more intricate, as acquiring new servers quickly was not a viable option. In cases where long-term resource planning fell short, promptly adding capacity became almost an impossible task. Consequently, service unavailability and significant financial losses were common occurrences. Even in instances of precise resource planning, the majority of the acquired additional resources remained largely underutilized. Here comes the flexibility of public clouds to the rescue. Utilizing cloud services allows companies to pay only for the resources they actually use within specific time frames, and they have the ability to scale their consumption up or down at any moment. People often try to compare the cost of purchasing a physical server with renting resources in the cloud based solely on CPU, RAM, and Storage metrics, which is not entirely accurate. Of course, in such cases, using the cloud may appear to be expensive. However, many factors are not taken into account in such a comparison, such as the cost of consumed electricity, the salaries of technical specialists who manage these resources, physical and fire safety, and so on. Ready to Accelerate Your Journey to the Cloud? Choose Gart as your trusted AWS migration partner for a seamless on-premise to AWS Cloud migration. Let's dive in! Drivers for AWS Cloud Migration Over the past few years, there has been a significant increase in companies' demand for cloud services, which is entirely logical considering the advantages that companies gain through AWS cloud migration. Businesses identify the following drivers that motivate them to migrate: Establishing a resilient infrastructure Gaining quick access to computing power and services High level of flexibility in infrastructure management Optimization and scalability Leveraging innovative solutions such as IoT, ML, AI Complexity and duration of implementing hardware solutions Cost reduction through the use of cloud technologies In summary, companies aspire to grow rapidly, enhance user experiences, implement digital transformation tools, and modernize their businesses. They reinvest the cost savings from infrastructure into developing their companies further. Nearly every migration is a challenging undertaking.  Business Outcomes after Migration Cloud technologies offer companies a range of advantages, including: Cost reduction compared to on-premise solutions (31%) Increased staff productivity and quick onboarding (62%) Enhanced flexibility in implementing new services (75%) However, migration projects for large companies are complex decisions that require a comprehensive approach, combining the application of specific services, methodologies, and expertise in chosen cloud technologies. Often, executing migration projects without proper management methodologies significantly complicates the process and substantially extends the project timelines. At Gart, we transform the migration process into a well-managed and conscious journey by offering a proven methodology, as a leader among cloud providers, integrating technical solutions with the company's business objectives, and enhancing the competence of clients when working in cloud environments. Moving forward, we will explore how to achieve a fast and effective migration to Amazon Web Services. Don't miss this opportunity to embrace the limitless possibilities of AWS Cloud with Gart by your side!. Contact Us AWS Migration Acceleration Program (MAP) For any organization, the key performance indicators for the successful implementation of new technologies typically revolve around stability, high availability, and cost-effectiveness. Hence, it is crucial to assess the company's IT infrastructure and business processes' readiness for cloud migration. To facilitate this process, AWS offers a specialized program called the AWS Migration Acceleration Program (MAP).   It is important to note that this program may not be applicable to all clients. For instance, migrating a single virtual server is unlikely to meet the requirements of this offer. However, for medium and large-scale companies seriously considering the adoption of cloud services, this program will be highly beneficial. In addition to the comprehensive approach to AWS cloud migration, the MAP program provides clients with a significant discount on resource usage for a duration of three years. The program comprises three main stages:  Assessment Mobilization (testing) Migration and modernization. Assessment During the assessment stage, the officially authorized AWS MAP partner conducts an inventory of the client's existing systems to develop a conceptual architecture for their migration to the cloud. A comprehensive business case is created, outlining how the infrastructure will look after the migration, the estimated cost for the client, and when it is advisable to transition from virtual machines to services. All client requirements regarding availability, resilience, and security are taken into account. Additionally, an evaluation of existing licenses, such as Oracle or Microsoft, is performed to determine whether it is beneficial to migrate them to the cloud or opt for renting them directly from the platform. As a result, the client receives exhaustive information about migration possibilities and potential cost savings in the cloud. In some cases, these savings can reach up to 70%. Typically, the assessment stage takes 3-6 weeks, depending on the project's complexity. Mobilization During the testing stage, a test environment is deployed in the cloud based on the developed architecture to verify the proposed solutions evaluated during the assessment phase. Migration and modernization After conducting all the tests, we move on to the final stage of the AWS MAP. At this stage, the production infrastructure is deployed in the cloud, and its optimization takes place. However, it's essential to continuously analyze and optimize the infrastructure on a regular basis. MAP AWS Benefits The AWS Migration Acceleration Program (MAP) offers several benefits, including: Comprehensive Assessment Clients receive a thorough evaluation of their IT infrastructure and business processes to assess readiness for AWS cloud migration. Cost Savings The program provides significant discounts on resource usage for three years, helping clients save costs during their migration journey. Conceptual Architecture A well-defined conceptual architecture is developed for the cloud migration, outlining the post-migration infrastructure and estimated costs. License Optimization Existing licenses, such as Oracle or Microsoft, are evaluated to determine the most cost-effective approach for their migration or rental on the cloud platform. Test Environment A test environment is set up in the cloud to validate the proposed solutions and ensure a smooth migration process. Production Deployment and Optimization After successful testing, the production infrastructure is deployed in the cloud and continuously optimized for performance and efficiency. Regular Analysis and Optimization The MAP ensures that infrastructure analysis and optimization are conducted regularly to maintain peak performance and cost-effectiveness. Conclusion: Making On-Prem to Cloud Migration Pay Off On-prem to cloud migration succeeds or fails less on the technology than on the discipline behind the decision-making: an honest inventory, a workload-by-workload strategy instead of a blanket mandate, and a cost governance practice that starts on day one rather than after the first surprising invoice. Done well, moving from on-premise to AWS still delivers real, measurable advantages in scalability, security posture, and operational efficiency — AWS’s own program data backs that up, and so does our experience running these migrations for clients across healthcare, fintech, and e-commerce. If your estate is more mixed — some workloads ready for AWS, others better left in place for now — our broader guide to the on-premise to cloud migration journey across AWS and Azure walks through that decision in more depth. And if you would rather have a second set of eyes look at your specific environment, our team is glad to talk it through — get in touch or browse our migration case studies for examples of how this plays out in practice. Read more: Cloud vs. On-Premises: Choosing the Right Path for Your Data Navigate the cloud with confidence! Our Cloud Consulting experts provide tailored solutions for migration, scalability, and security. Ready to elevate your business? Get in touch for a transformative consultation. 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.

What are the common mistakes to avoid when building an application in the cloud? Some of the most frequent cloud development mistakes include deploying all application components on a single server, exposing databases to the public internet, using dynamic IP addresses, skipping automated backups, and building on architectures that don't support horizontal scaling or multi-AZ availability. These issues can cause costly downtime, security breaches, and performance bottlenecks. How Cloud Computing Changed the Game for SMBs Cloud computing has revolutionized how small and medium-sized businesses (SMBs) build applications. Platforms like AWS have drastically lowered the barrier to entry, allowing companies to create custom solutions that meet their unique needs without investing in physical infrastructure. However, while building applications in the cloud seems simple, many organizations unknowingly fall into traps that lead to bloated costs, instability, and poor performance. This case study explores one such real-world example and breaks down how things could’ve been done better — and how you can avoid making the same mistakes. Key Stakeholders in Cloud App Development Every cloud project involves a variety of decision-makers and implementers. In this scenario, we had three primary stakeholders: 1. The SMB Owner The visionary who wanted a custom-built application to support internal inventory and order management. Their goal was a lean, efficient tool tailored to business operations — without the recurring fees of an off-the-shelf SaaS product. 2. The Development Team An outsourced group responsible for coding the backend logic, designing the user interface, and setting up the database. They had experience with Microsoft server environments and chose AWS for deployment. 3. The Managed Service Provider (MSP) This team managed the AWS infrastructure. They maintained the hosting environment and had oversight over the deployed resources, often coordinating between the SMB owner and the dev team. Cloud Architecture Overview of the Inventory App The team collaborated to build a cloud-hosted inventory and order management system with three main layers: Web Frontend – The user interface accessed via browser Business Logic Tier – The middle layer that processes requests Database Tier – Stores all business-critical data The Initial Cloud Setup The app was deployed entirely on a single Microsoft Server EC2 instance, hosted in AWS. This "all-in-one" setup, while simple, introduced several significant risks — many of which only became apparent after launch. Why Hosting Everything on One EC2 Instance Was a Critical Mistake Encapsulation of All Tiers All three application layers — frontend, business logic, and database — lived on one virtual machine. That created a single point of failure. If that one instance went down, the entire application would become inaccessible. Lack of Scalability Because everything was on a single EC2 instance, horizontal scaling was impossible. The only way to handle more users or traffic was by vertically scaling (adding more CPU/RAM), which quickly becomes expensive and inefficient. Single Availability Zone (AZ) Risk All resources were tied to one AWS Availability Zone. If that AZ experienced downtime — due to maintenance or failure — the application would go down with it. No redundancy meant no resilience. Public Exposure of Sensitive Components The EC2 instance was directly accessible from the internet. This meant the database layer was publicly reachable — a major security vulnerability. A better approach would isolate the logic and database tiers in private subnets. Dynamic IP Address Usage Instead of assigning a fixed Elastic IP, the app used a dynamic public IP. As a result, every time the instance rebooted, its IP could change — breaking the GoDaddy domain’s DNS mapping. No Automated Backups There were no snapshot policies or backup strategies in place. If the EC2 instance failed or was deleted, the entire application — and its data — would be lost permanently. Summary: Problematic vs Best Practice Architecture Here’s a simple table summarizing the major issues: Component/FeaturePoor ImplementationBest Practice RecommendationApp HostingAll tiers on one EC2 instanceDecouple into separate tiersIP AddressDynamic IPUse Elastic IPAvailabilitySingle AZMulti-AZ deploymentSecurityPublic access to all componentsPrivate subnets with minimal public exposureBackup StrategyNoneUse automated snapshots or RDS backupsScalabilityVertical scaling onlyUse autoscaling groups for horizontal scaling How These Cloud Missteps Affected Performance and Cost Performance Limitations Because the app couldn’t scale horizontally, its performance dipped significantly under moderate load. Adding resources manually was the only option — and it wasn’t cheap. Security Vulnerabilities Leaving all tiers accessible from the public internet exposed the app to malicious scans and attacks. Security best practices in the cloud demand network isolation and firewall policies. Downtime Risk A single Availability Zone meant no failover path. If something happened in that AZ — and it does happen — the entire app went offline. High Cost with Low Efficiency The monthly AWS bill exceeded $250. For what was essentially a lightweight inventory tool, this cost was unjustifiable — especially since a properly designed architecture could run for much less. Why These Implementation Issues Matter Performance Limitations: without horizontal scaling, the application risked overload during simultaneous user access. The only workaround was vertical scaling, which increased costs significantly. Availability Risks: hosting in a single AZ made the application vulnerable to downtime if the AZ experienced issues. Security Concerns: direct exposure to the public internet increased the risk of cyberattacks. Isolating tiers in private subnets could have mitigated this. Configuration Challenges: the dynamic IP address required manual updates to maintain URL functionality. Data Loss Risks: without automated snapshots, recovering from an instance failure was nearly impossible. What Would a Better AWS Cloud Architecture Look Like? Decoupled Design Each application tier should have its own resource — or be hosted using managed services: Frontend: Host using S3 and CloudFront for better performance and lower cost. Business Logic: Deploy on an EC2 instance in a private subnet — or use AWS Lambda for serverless architecture. Database: Use Amazon RDS with encryption, backup, and monitoring baked in. Enhanced Architecture Improvements Autoscaling Groups: Allow the logic tier to scale based on demand. Multi-AZ Deployment: Ensures high availability across AWS regions. Private Subnets + NAT Gateway: Secure internal components. Elastic IP: Ensures static access for DNS mapping. Automated Snapshots & RDS Backups: Ensures data resilience and fast recovery. Key Improvements: Autoscaling Groups: separate EC2 instances for each tier could be placed in autoscaling groups to dynamically manage demand. Multi-AZ Deployment: distribute resources across multiple AZs to ensure high availability. Enhanced Security: use private subnets for the logic and database tiers, and apply security group rules to minimize the attack surface. Elastic IP Addressing: associate an Elastic IP with the EC2 instance to ensure URL consistency. Automated Backups: configure regular snapshots to prevent data loss and simplify recovery. Real-World Fixes Implemented by Gart When Gart Solutions stepped in, the client needed to stabilize the current environment during their migration to a new solution. Here's what we immediately fixed: Created EC2 Snapshots for manual backup Assigned an Elastic IP for DNS stability Updated the GoDaddy DNS Record to the new static IP While these fixes improved the existing environment, they also highlighted how the app could have been built more effectively from the start. Get a sample of IT Audit Sign up now Get on email Loading... Thank you! You have successfully joined our subscriber list. Conclusion: Build Smarter, Not Just Faster in the Cloud This story isn’t just about one company’s missteps — it’s a cautionary tale for any organization building in the cloud. Misusing cloud infrastructure can be costly, risky, and inefficient. But with thoughtful architecture, security-focused design, and scalable components, even small businesses can build reliable, high-performing cloud apps. Don’t treat the cloud like a virtual closet to stuff everything into. Treat it like the powerful ecosystem it is — and you’ll unlock the speed, resilience, and savings it promises. Top 5 Lessons to Avoid Cloud App Failures Never host all tiers on one instance Avoid public exposure of sensitive services Use Elastic IPs for stable DNS resolution Automate your backups Design for scalability and availability from day one Have You Faced Cloud Architecture Challenges? What were your takeaways? Share your experiences — or reach out if you'd like help optimizing your cloud strategy.