European Tech Independence & Cloud Control: What Are Europe’s Alternatives for Achieving Digital Autonomy?

Organizations often expect to cut costs when migrating IT infrastructure from on-premises setups to the cloud. However, the reality can be starkly different. Various cost traps can emerge during cloud transformation, leading to unexpectedly high expenses if not carefully managed.  In this article, we will explore the most common cloud cost traps and provide strategies to avoid cloud expenditures during cloud transformation. Estimating Cloud Transformation Expenses  Forecasting expenses during a cloud transformation can be challenging. In traditional IT setups, cost estimation is relatively straightforward, encompassing fixed costs such as data center rent, hardware, and licenses. Conversely, in a cloud environment, you pay based on usage, which can fluctuate greatly, complicating cost predictions.  This variability often leads to unforeseen cost spikes during cloud transformation.  At Gart Solutions, we help numerous organizations navigate their cloud transformation journeys and frequently encounter five key mistakes that can drive up costs.  Common Cloud Costs Optimization Traps Happening There: 1. The "Lift and Shift" Approach  A common mistake is migrating existing setups to the cloud without modifications, known as a "lift and shift." This method usually leads to high costs because it fails to leverage the cloud's unique advantages. Instead, organizations should modernize their solutions through "refactoring." Refactoring involves changing or completely replacing applications or systems to exploit the cloud's benefits such as scalability, elasticity, self-service, and measurability. By optimizing resources for the cloud, organizations can achieve greater flexibility, efficiency, and cost savings.  2. Choosing the Wrong IT Architecture Setup Selecting the right architecture for a cloud environment is critical. IT architecture must be carefully planned to maximize cloud benefits. Determining how services interact and which ones to use requires an architecture tailored to the organization’s needs. Poor architectural choices can result in an environment that does not meet security and scalability requirements, leading to costly adjustments. Hence, it's vital to seek expert advice, critically review recommendations, and explore alternatives that do not compromise functionality or performance.   Contact Gart & get the IT Infrastructure Consulting. Quick and long-term wins are guaranteed.   3. Overreliance on Enterprise Versions  Organizations often default to enterprise versions of cloud services without assessing their actual needs. This can result in unnecessarily high costs. Before opting for enterprise versions, it's crucial to evaluate if standard versions can meet the requirements. Contact Gart & assess cloud services carefully.  4. Uncontrolled Capacity Planning  Accurately predicting capacity needs is a common challenge, where previous expertise is needed. Capacity requirements can vary — constant for some, linear for others, exponential for a third, and seasonal for a fourth. These variations can lead to over- or under-provisioning of resources, resulting in additional costs. Effective capacity planning involves basic estimates and continuous monitoring to detect changes in needs and adjust resources accordingly. Utilizing the built-in capabilities of cloud services for business alignment requires the right expertise.   Contact Gart to assess your resources and requirements and make optimal capacity planning.  5. Lack of Appropriate Skills Cloud transformation increases IT delivery complexity and requires new skills. Without the right expertise, projects can become inefficient, leading to wrong decisions and increased costs.  Contact Gart - we are the trusted partner in cloud consulting, migration, and cost optimization. 6. Underestimating Data Transfer Costs One often overlooked cost trap is data transfer. While moving data to the cloud might seem straightforward, the costs associated with data egress (data leaving the cloud) can be substantial. Organizations should be aware of these charges and plan data transfer strategies accordingly, such as optimizing data placement and minimizing data transfer needs.  7. Neglecting Long-term Cost Implications  Focusing solely on immediate cost savings can lead to higher long-term expenses. Organizations should consider the long-term implications of their cloud choices, including potential costs associated with scaling, maintenance, and upgrades. A comprehensive cost-benefit analysis that includes future scenarios can help in making more informed decisions.  8. Ignoring Security and Compliance Costs  Security and compliance are critical in cloud environments, and neglecting these aspects can lead to significant expenses. Ensuring that cloud deployments comply with regulatory standards and implementing robust security measures can prevent costly breaches and fines. Investing in security tools and expertise upfront is crucial for avoiding unexpected costs later.  Contact for consulting in security, architecture, and cloud technologies. Prevent unnecessary expenses and ensure a smooth transition.  9. Failing to Monitor and Optimize Usage  Continuous monitoring and optimization of cloud usage are essential for controlling costs. Without proper monitoring, organizations can easily overspend on unnecessary resources. Utilizing cloud management tools and implementing policies for regular audits and optimizations can help keep costs in check.  10. Overlooking Vendor Lock-in Risks  Vendor lock-in occurs when an organization becomes overly dependent on a single cloud provider, making it difficult and costly to switch providers. To mitigate this risk, organizations should adopt a multi-cloud strategy or ensure that their architecture allows for flexibility and portability across different cloud platforms.  11. Underutilizing Reserved Instances and Savings Plans Cloud providers offer reserved instances and savings plans that can significantly reduce costs for predictable workloads. However, many organizations fail to take advantage of these options. By analyzing usage patterns and committing to long-term plans, organizations can achieve substantial savings compared to on-demand pricing.  12. Over-Provisioning Resources Over-provisioning, or allocating more resources than needed, is a common cost trap. This often results from a lack of understanding of actual usage requirements. Implementing auto-scaling and right-sizing strategies can help in dynamically adjusting resources based on real-time demand, thus avoiding unnecessary expenses.  13. Lack of Governance and Policies  Effective governance and policies are crucial for managing cloud costs. Without proper governance, organizations can face uncontrolled spending and resource mismanagement. Establishing clear policies for resource usage, cost allocation, and accountability ensures that cloud resources are used efficiently, and costs are kept under control.  14. Neglecting to Decommission Unused Resources  Failing to decommission unused or idle resources can lead to wasted spending. Regularly reviewing and cleaning up unused instances, storage, and other resources can help in reducing unnecessary costs. Automation tools can assist in identifying and decommissioning these resources.  15. Ignoring Cloud Cost Management Tools Most cloud providers offer cost management and optimization tools, but organizations often overlook these valuable resources. Utilizing these tools can provide insights into spending patterns, help identify cost-saving opportunities, and enable more effective budgeting and forecasting.  16. Misjudging Data Storage Costs  Data storage costs can add up quickly, especially if organizations do not manage their data efficiently. Implementing data lifecycle policies, such as archiving old data and deleting redundant data, can help control storage costs. Additionally, selecting the appropriate storage class based on access frequency and performance needs is essential.  17. Overlooking Software Licensing Costs  Cloud transformation can sometimes lead to increased software licensing costs, particularly if organizations do not review their licensing agreements carefully. It's important to understand the licensing implications of moving to the cloud and to negotiate contracts that align with cloud usage patterns.  18. Inadequate Backup and Disaster Recovery Planning  Backup and disaster recovery are critical components of cloud strategy, but inadequate planning in these areas can lead to unexpected costs. Organizations should ensure that their backup and recovery plans are cost-effective and align with their overall cloud strategy. This includes selecting the right tools and services for efficient data protection and recovery.  19. Failing to Leverage Cloud Provider Discounts  Cloud providers often offer discounts for committed usage, volume purchases, and other incentives. Failing to leverage these discounts can result in higher costs. Organizations should actively seek out and take advantage of available discounts to optimize their cloud spending.  Contact Gart and calculate cloud discounts for your case. 20. Not Considering Hidden Costs  Hidden costs, such as those related to network latency, data retrieval, and specialized support, can significantly impact the overall cloud budget. Organizations should thoroughly assess all potential costs, including those that may not be immediately apparent, to avoid budget surprises.  Final words  Cloud transformation requires specialized expertise & careful planning.   Contact us to address all organizational needs for a successful cloud transformation.   Gart Solutions can help your organization avoid common cost traps and achieve business objectives efficiently. Review our latest Case Studies of our cloud migration & cost optimization projects.

The way businesses store and manage data is undergoing a significant shift. Traditionally, companies relied on on-premises infrastructure – physical servers and hardware located within their own facilities. However, the rise of cloud computing offers a compelling alternative. Let's delve into both options with facts and figures to help you decide which is best for your needs. Cloud Computing Cloud computing refers to the delivery of computing services—including servers, storage, databases, networking, software, analytics, and intelligence—over the Internet ("the cloud") to offer faster innovation, flexible resources, and economies of scale. 94% of enterprises use cloud services as of 2019 (Source: Flexera). Cloud resources are elastic. You can easily scale storage and processing power up or down based on your needs. Gartner predicts that by 2025, 85% of enterprise IT strategies will be cloud-first [source: Gartner]. Cloud eliminates the upfront costs of buying and maintaining hardware. You only pay for the resources you use, leading to significant potential savings. A 2023 Flexera report found that businesses can save up to 30% on IT infrastructure costs by migrating to the cloud. Cloud providers handle software updates and security patches, freeing up your IT staff for other tasks. Access your data and applications from anywhere with an internet connection, promoting remote work and collaboration. On-Premises Infrastructure On-premises or bare metal refers to a computing infrastructure that is installed and run on computers on the premises of the organization using the software, rather than at a remote facility or in the cloud. The global bare metal cloud market was valued at $5.6 billion in 2021, and is projected to reach $56.6 billion by 2031, growing at a CAGR of 26.1% from 2022 to 2031. (Source: Verified Market Research). On average, organizations using on-premises infrastructure spend 55% of their IT budgets on maintenance, compared to 45% for cloud users (Source: Deloitte). While cloud computing is gaining traction, on-premises solutions still hold value for some businesses: You have complete control over your hardware and software, allowing for high levels of customization. Some businesses might prefer to keep sensitive data in-house, perceived to be more secure. However, with advanced security measures, reputable cloud providers offer robust data protection. Certain industries may have strict data residency regulations that favor on-premises storage. Cost Considerations Cloud services operate on an operational expenditure (OpEx) model, where you pay for resources and services as you use them. This eliminates the need for large upfront investments. On-premises solutions require substantial capital expenditure (CapEx) for purchasing and setting up hardware, data centers, and related infrastructure. This includes costs for servers, storage, networking equipment, and facilities. Starting with cloud services often requires minimal initial financial commitment. You can quickly provision resources without significant capital expenditure, making it accessible for startups and projects with limited budgets. The hardware you buy will depreciate over time, which can affect long-term financial planning and capital allocation. While on-premises may seem cheaper upfront due to the absence of CapEx, the TCO (Total Cost of Ownership) can be higher due to factors like: Hidden Costs: On-premises solutions often have hidden costs like power, cooling, physical space, maintenance, and staff for ongoing management. Hardware Refresh Cycles: Replacing outdated hardware can be a significant ongoing expense. Underutilized Resources: Businesses often overprovision hardware to handle peak loads, leading to wasted resources and costs. Here's a table summarizing the key cost considerations: FeatureCloud ComputingOn-premises/Bare MetalInitial InvestmentLow (OpEx)High (CapEx)Hidden CostsLowerHigher (power, cooling, space, staff)Hardware RefreshManaged by providerResponsibility of the organizationResource UtilizationPay-per-usePotential for underutilized resourcesScalabilityFlexible and cost-effectiveCan be slow and expensive Performance and Scalability: Cloud vs. On-premises Cloud computing offers elasticity, allowing you to rapidly scale resources (processing power, storage) up or down based on real-time demand. This ensures optimal performance during peak loads without sacrificing resources during low usage periods. A 2023 study by Flexera found that 73% of businesses reported improved application performance after migrating to the cloud. [Source: state of the cloud ON Flexera flexera.com] Examples: ▪️ You can choose from a range of instance types optimized for different workloads, such as compute-optimized, memory-optimized, and storage-optimized instances. For example, an m5.2xlarge instance provides 8 vCPUs and 32 GB of memory, suitable for high-performance computing tasks. ▪️ Azure offers virtual machine sizes tailored for specific scenarios, such as the D-series for general-purpose workloads and the H-series for high-performance computing. Bare metal servers often provide superior performance for certain high-demand workloads due to their dedicated hardware. This can be critical for applications requiring high I/O throughput, low latency, or substantial computational power. With bare metal, you have the flexibility to configure hardware to meet specific performance requirements. This is particularly beneficial for specialized applications, such as machine learning models or high-frequency trading platforms. Examples: ▪️ A bare metal server with Intel Xeon Platinum CPUs and NVMe SSDs can handle large-scale databases or data-intensive applications with minimal latency. For instance, benchmarks show that a single bare metal server can achieve up to 1 million IOPS (input/output operations per second) compared to 100,000 IOPS for a typical cloud SSD instance. ▪️ IBM offers customizable bare metal servers with up to 192 GB of RAM and 16 vCPUs, providing the raw performance needed for demanding workloads. These servers are often used for tasks that require consistent, high-speed performance without the overhead of virtualization. Scaling on-premises infrastructure typically requires purchasing and installing additional hardware. This process involves significant planning, procurement, and installation time. For example, scaling from a small data center to a larger one may involve several months of lead time for new hardware and infrastructure. Data Sovereignty and Compliance Considerations Major cloud providers comply with a range of international and industry-specific standards. For example: AWS Compliance: AWS holds certifications such as ISO 27001, SOC 1/2/3, GDPR compliance, and HIPAA compliance. Azure Compliance: Microsoft Azure is compliant with standards including ISO 27001, SOC 1/2/3, GDPR, and HIPAA. Google Cloud Compliance: Google Cloud complies with standards like ISO 27001, SOC 1/2/3, GDPR, and HIPAA. Read more: Gart’s Expertise in ISO 27001 Compliance Empowers Spiral Technology for Seamless Audits and Cloud Migration Cloud providers offer data residency options, allowing organizations to choose the geographical location where their data is stored. For instance, AWS provides data centers across various regions globally, and users can select the region that aligns with their data sovereignty requirements. Cloud providers ensure compliance with local data protection laws, such as the EU's General Data Protection Regulation (GDPR), which mandates that data of EU citizens must be stored within the EU or in countries with adequate protection levels. On-premises environments require organizations to ensure compliance with local and industry regulations. This often involves implementing complex data protection measures and ensuring that all aspects of the infrastructure adhere to regulatory standards. With on-premises infrastructure, organizations have complete control over their data and its location, which can be advantageous for meeting specific data sovereignty requirements. However, this also means that the organization is fully responsible for implementing and maintaining compliance measures. A study by the International Association for Privacy Professionals (IAPP) found that the average cost of GDPR compliance for organizations is approximately $1.5 million. Cloud providers often absorb some of these compliance costs through their shared responsibility models, potentially reducing the financial burden on individual organizations. Cloud Provider Security Measures vs. In-House Security In cloud environments, security is a shared responsibility between the cloud provider and the customer. Providers like AWS, Azure, and Google Cloud are responsible for the security of the cloud infrastructure, including physical security, network security, and virtualization layers. Customers are responsible for securing their data, applications, and configurations within the cloud. On-premises security involves dedicated resources for managing physical security, network security, and data protection. This includes physical access controls, firewalls, intrusion detection systems, and regular security audits. According to a Ponemon Institute study, organizations with in-house security teams spend an average of $3.6 million annually on security, compared to $2.6 million for organizations using managed security services. This highlights the potential cost advantage of cloud security solutions, where many security services are included as part of the subscription. Use Cases and Considerations: Cloud vs. On-premises Use CaseCloud ComputingOn-premisesStartups and Rapid DevelopmentIdeal due to low upfront costs, scalability, rapid deployment, and reduced IT burdenLess suitable due to high upfront costs and inflexibilityHigh-Traffic ApplicationsWell-suited for handling traffic spikes and unpredictable workloadsCan be challenging and expensive to scale for unpredictable trafficStable WorkloadsGood choice, with cost optimization options for stable workloadsSuitable, but scaling can be slow and expensiveVariable DemandsGood choice due to inherent elasticityLess suitable due to challenges in scaling up or down The Future is Hybrid Many businesses are adopting a hybrid approach, combining cloud and on-premises infrastructure. This allows them to leverage the benefits of both: cost-effectiveness, scalability, and control over sensitive data. FeatureCloud ComputingOn-premises/Bare MetalDeployment ModelOff-site, delivered over the internetOn-site, within your data centerScalabilityEasy to scale up or down resourcesScaling can be slow and expensiveCostPay-as-you-go modelHigh upfront costs for hardware, software, and IT staffAccessibilityAccessible from anywhere with an internet connectionAccess might be restricted to the local networkSecurityRobust security features offered by cloud providersRequires strong internal security measuresMaintenanceManaged by the cloud providerRequires in-house IT staff for maintenanceControlLess control over hardware and softwareFull control over hardware and softwareCustomizationLimited customization optionsHighly customizable Here are some additional factors to consider: ▪️ Your IT Expertise: Cloud computing eliminates the need for in-house IT expertise for infrastructure management. ▪️ Your Industry: Regulations in certain industries might influence your choice. For example, healthcare organizations might have stricter data privacy requirements. In Conclusion Cloud computing is rapidly changing the IT landscape. With its flexibility and cost-efficiency, it's a strong contender for most businesses. However, on-premises infrastructure still plays a role for specific needs. Carefully evaluate your requirements and consider a hybrid approach to gain the best of both worlds.

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.