How to optimize AWS costs? A practical step-by-step guide + proven cost-saving strategies

Optimizing Amazon Web Services costs is one of the biggest challenges for companies using the cloud. Spending on AWS services can quickly get out of control without proper management, planning and monitoring. A well-implemented optimization strategy can not only significantly reduce monthly bills, but also improve infrastructure performance and security.

In 2025, as competition for computing resources intensifies and power costs for data centers rise, the ability to effectively manage cloud spending becomes a key competitive advantage. At the same time, the continued evolution of AWS services - from new Graviton3 instances to more flexible Savings Plans options to advanced FinOps tools - is opening up new opportunities for optimization.

Shortcuts

• How do you start AWS cost optimization from scratch?
• Why is expense monitoring the cornerstone of savings at AWS?
• How to use AWS Cost Explorer for cost analysis?
• What are Reserved Instances and how do they reduce bills by up to 72%?
• When is it worth using Spot Instances and how much can you save?
• How does AWS Auto Scaling work and why does it reduce unnecessary costs?
• How to properly size an EC2 instance to meet the needs of the application?
• How to optimize data storage costs in S3 and EBS?
• Are Savings Plans better than Reserved Instances?
• How to identify and remove unused resources in AWS?
• Why is asset tagging crucial to cost control?
• How to use AWS Trusted Advisor for automatic optimization?
• When is it worth migrating workloads to a serverless (Lambda) architecture?
• How to reduce the cost of transferring data between AWS regions?
• Why are regular infrastructure audits a must?
• How to plan a budget using AWS Budgets?
• What is FinOps and how to implement this methodology in AWS?
• When is it a good idea to engage outside experts for optimization?

How do you start AWS cost optimization from scratch?

Embarking on the adventure of AWS cost optimization requires, first of all, an understanding of the current state of expenses. The first step is to analyze exactly what is actually generating costs in your environment. Start by logging into the AWS Management Console and navigating to the Billing & Cost Management section, where you will find detailed information about your expenses.

Here is a concrete example of the first approach to cost analysis:

• Open the AWS console and go to “Billing & Cost Management”

• Select “Cost Explorer”

• Configure “Monthly costs by service” view for the last 3 months

• Identify 3-5 services that generate the highest costs (e.g., for a typical organization: EC2 - 45%, RDS - 18%, S3 - 12%, data transfer - 8%, other - 17%)

• For each of these services, use the “Group by” function according to dimensions such as “Region,” “Instance Type” or “Purchase Option.”

Properly organizing your AWS account structure is another fundamental step. If you manage a larger organization, consider implementing AWS Organizations with a tiered structure:

This structure allows for precise cost allocation and the implementation of Service Control Policies (SCPs) limiting which services and regions can be used in which accounts, eliminating unnecessary expenses. For smaller organizations, it will be crucial to implement a consistent resource tagging system.

Trap to avoid: An account structure that is too fragmented can lead to poorer visibility of total costs and the loss of some volume discounts. Conversely, too flat a structure makes it difficult to manage entitlements and allocate costs. Balance is key.

Once you understand your cost structure and organize your environment accordingly, it’s time to determine the key metrics (KPIs) you will monitor. Consider the following metrics:

• Cost per business unit (e.g., cost per transaction, customer, user)

• Ratio of spending on non-productive to productive environments (optimally 20-30%)

• Percentage of resources covered by reservations or Savings Plans (target: 70-80%)

• Utilization rate of reserved instances (target: >95%)

• Wastage rate (expenditure on unused/idle resources)

The final step in the initial optimization is to identify savings goals and develop a specific action plan. An example of a 30-day optimization plan might look like the following:

• Days 1-7: Audit and identify quick wins (unused resources, oversized instances)

• Days 8-14: Implement basic automation (shut down non-production environments outside of business hours)

• Days 15-21: Analysis of usage patterns for Reserved Instances and Savings Plans

• Days 22-30: Implementation of long-term optimization strategies

This type of phased approach makes it possible to systematically reduce costs without the risk of destabilizing the environment.

📚 Read the complete guide: Cloud Security / AWS: Bezpieczeństwo chmury publicznej - AWS, Azure, best practices

Why is expense monitoring the cornerstone of savings at AWS?

Spend monitoring in AWS is the absolute foundation of effective cost optimization. Without accurate tracking of where and how money is being spent, making informed decisions becomes impossible. AWS provides a number of tools that enable detailed cost monitoring - from the basic AWS Cost and Usage Report (CUR), to AWS Budgets, to the more advanced Cost Explorer. However, understanding the potential and limitations of each of these tools is key.

AWS Cost and Usage Report (CUR) is the most detailed source of cost data, providing information with hourly accuracy and containing thousands of dimensions of analysis. Setting up CUR requires several steps:

CUR generates CSV or Parquet files of up to gigabytes per month, which can pose analytical challenges. Therefore, for daily monitoring, Cost Explorer, which provides an intuitive graphical interface, but with smaller data granularity (daily minimum), is better suited.

Three common pitfalls should be avoided when implementing effective cost monitoring:

• Delay Trap - By default, Cost Explorer data can be delayed by 24-48h, making it difficult to respond immediately. Consider enabling Cost Explorer Hourly Granularity (a new feature) for an additional fee if you need faster data.

• The aggravation trap - overly aggregated data can mask significant problems. For example, stable total costs can hide the fact that a decrease in EC2 costs has been offset by an increase in RDS costs.

• The trap of information overload - too many dashboards and alerts lead to ignoring them. Start by monitoring 3-5 key indicators and gradually expand the system.

An effectively configured monitoring system should include:

• Daily reports showing cost trends for key services and resources

• Automatic alerts to detect anomalies, such as a sudden 20% increase in spending compared to the average over the past 7 days

• Weekly dashboards for development teams with the cost of their resources

• Monthly trend report for management with forecast for the following months

A model example of a monitoring flow would look as follows:

• AWS CUR generates detailed cost data and saves it to S3

• Lambda function processes the data and loads it into a database (e.g. Amazon RDS)

• Amazon QuickSight or a third-party tool (such as Tableau) creates dashboards for different audiences

• Automatic alerts are sent by Amazon SNS to relevant teams

• Monthly review process identifies trends and optimization opportunities

In addition to native AWS tools, consider third-party solutions such as CloudHealth, Cloudability or Spot.io (formerly NetApp), which offer advanced trend analysis, savings recommendations and provisioning management. While they come with additional costs, for organizations spending more than $10,000 per month on AWS, their value often outweighs the investment.

AWS cost monitoring foundation - summary

• Regular reviews: Check costs at least once a week, don’t wait for an invoice

• Alerts and notifications: Set up automatic alerts for unusual spending spikes

• Data granularity: Balance detail with clarity in reports

• Team accountability: Distribute responsibility for cost monitoring to all teams

• Automation: Implement automated processing and visualization of cost data

How to use AWS Cost Explorer for cost analysis?

AWS Cost Explorer is a powerful tool that allows you to deeply analyze and visualize spending trends in the cloud. To get started with Cost Explorer, go to the AWS console, select Billing and Cost Management, and then Cost Explorer. The tool offers an intuitive graphical interface that allows you to filter, group and analyze costs by various dimensions - services, regions, tags, instance types or even purchase options. This flexibility allows you to pinpoint which infrastructure elements are generating the highest costs.

One of the most important features of Cost Explorer is the ability to analyze historical spending trends. By default, the tool shows data from the last 13 months, which gives you a broader perspective and allows you to identify seasonal patterns or long-term trends. You can also create forecasts of future costs based on historical data - extremely useful for budget planning. Cost Explorer allows you to export data to CSV format for further analysis in external tools or integration with financial reporting systems.

An advanced feature of Cost Explorer is the analysis of savings from Reserved Instances (RI) and Savings Plans. The tool generates recommendations to purchase RIs or Savings Plans based on historical usage, which can significantly reduce costs. In addition, Cost Explorer allows you to monitor the actual savings achieved through already purchased Reserved Instances and their utilization rate. This is invaluable information that helps you decide whether to renew, purchase additional ones or cancel certain bookings.

To maximize the potential of Cost Explorer, it is worth experimenting regularly with different views and filters. For example, analyzing costs by tag (tags) can reveal which teams, projects or applications are generating disproportionately high costs. Grouping by instance type can help identify opportunities to consolidate or resize instances. Remember, Cost Explorer is not just an expense tracking tool, but more importantly an analytics platform that provides actionable insights - specific tips for optimization.

What are Reserved Instances and how do they reduce bills by up to 72%?

Reserved Instances (RI) is one of the most effective methods of reducing costs in AWS, offering significant discounts compared to On-Demand instances. The basic principle of Reserved Instances is simple - in exchange for a commitment to use certain resources for a fixed period (usually 1 or 3 years), you receive a discount of up to 72%. This solution works well for workloads with predictable and stable resource requirements, such as production environments, databases or core infrastructure.

AWS offers several types of Reserved Instances, which vary in flexibility and level of discount. Standard RIs provide the greatest savings, but require careful planning - they are assigned to a specific instance type and region. Convertible RIs offer a slightly smaller discount, but allow you to change the instance family, operating system and tenancy over the course of the reservation. This flexibility is invaluable in a dynamically changing environment. In addition, you can choose a payment option - total upfront payment (the largest discount), partial upfront payment or no upfront payment (the smallest discount, but still significantly lower than On-Demand).

The key to getting the most out of Reserved Instances is to carefully analyze usage patterns before purchasing. It is recommended that you analyze at least 30 days of usage history to identify stable, baseline workloads that run 24/7. You can use AWS Cost Explorer recommendations that suggest specific reservations based on historical resource usage. Remember that you don’t need to reserve 100% of your infrastructure - a good practice is to have 60-80% of your resources covered by RI, leaving a margin for On-Demand or Spot instances to handle variable workloads.

It’s also worth remembering that Reserved Instances can be modified and replaced during the reservation period. For Convertible RI, you can replace them with other types as your application requirements change or as AWS introduces new, more efficient instance families. For Standard RI, while you can’t change the instance family, you can still modify the availability zone, instance size (within the same family) or network (EC2-Classic/VPC). This flexibility ensures that your long-term commitments can evolve with your business needs.

Reserved Instances - the key to the greatest savings

• Commitment = savings: the longer the booking period and the larger the upfront payment, the higher the discount (up to 72%)

• Match the type to your needs: Standard RI for stable, unchanging loads; Convertible RI for changing requirements

• Optimal coverage: Reserve 60-80% of stable resources, serve the rest with On-Demand and Spot instances

• Analyze regularly: check RI utilization and modify the booking portfolio as needs change

When is it worth using Spot Instances and how much can you save?

Spot Instances are unused AWS computing resources offered at discounts of up to 90% compared to On-Demand pricing. This impressive savings makes Spot Instances an extremely attractive solution for many types of workloads. For a concrete comparison: a c6i.2xlarge instance (8 vCPU, 16 GB RAM) in the eu-west-1 region costs about $0.34/hour in the On-Demand model, while the same instance in the Spot model can cost as little as $0.03-0.07/hour, which translates into monthly savings of $220-$250 on a single instance.

A key feature of Spot Instances is their variable availability - AWS can interrupt such an instance with two minutes’ notice when resources are needed for an On-Demand or Reserved instance. It is this uncertainty of availability that poses the main implementation challenge. The interrupt rate varies significantly by instance type and region - some configurations have a historical interrupt rate of less than 5%, while others can exceed 20%.

The application architecture must be designed with Spot Instances in mind. Here are specific implementation patterns that work best:

• Queuing architecture with job caching

In this pattern, tasks are placed in an Amazon SQS queue, and a dynamic group of Spot Instances processes them as they become available. If an instance is aborted, the unfinished task is returned to the queue.

• Checkpoint and resume For long-running tasks, implement regular status saving (checkpointing) in persistent storage (e.g., S3):

• Hybrid architecture with Spot and On-Demand Use Spot for the processing layer, but keep critical components (e.g., databases, application servers) on On-Demand or Reserved instances.

Potential Pitfalls in Implementing Spot Instances:

• The hidden cost trap: Although the instances themselves are cheaper, the additional costs associated with interrupt handling and more complex architecture can reduce the actual savings. Always calculate total cost of ownership (TCO).

• Stability trap: Historically low interrupt rates can give a false sense of security. Build systems assuming interruptions will occur at the worst possible time.

• Migration trap: Moving existing applications to Spot without redesigning the architecture usually fails. Better to start with new, non-production workloads.

Modern AWS tools significantly simplify the use of Spot Instances. EC2 Auto Scaling with Spot and On-Demand mix allows you to define a minimum base capacity on On-Demand, with additional capacity on Spot. AWS Batch automatically manages the mix of instances for batch jobs. The latest additions are EC2 Capacity Blocks, which allow you to reserve larger blocks of Spot capacity in advance for a specified period of time.

From third-party tools, consider Spot.io (NetApp), which offers advanced Spot interrupt prediction algorithms and fleet management, or CloudHealth, which helps monitor usage and savings from Spot Instances.

In practice, many organizations are successfully implementing the hybrid model:

• 60-70% of the base load is covered by Reserved Instances

• 20-30% of variable load handle Spot Instances

• 5-10% of critical load remains on On-Demand instances

Such a strategy can reduce overall EC2 costs by up to 60-70% while maintaining a highly reliable environment.

How does AWS Auto Scaling work and why does it reduce unnecessary costs?

AWS Auto Scaling is an intelligent mechanism that automatically adjusts the number of active resources (usually EC2 instances) to the actual demand of the application. Its operation is based on continuous monitoring of system load and dynamic response to changes - when traffic increases, Auto Scaling adds new instances, and when it decreases, redundant resources are shut down. This flexibility has a direct cost impact, eliminating the traditional “stock” approach, where infrastructure must be sized for peak load, even if it occurs sporadically.

Auto Scaling configuration starts with the creation of groups (Auto Scaling Groups), which define scaling parameters: the minimum and maximum number of instances, the desired initial capacity and the instance configuration template. You then define scaling policies that decide when and how the group should scale. AWS offers several types of policies: goal-tracking scaling (e.g., keeping average CPU utilization at 70%), step scaling (different actions for different threshold metrics), and predictive scaling, which analyzes historical load patterns and adjusts capacity in advance to anticipated peaks.

Properly configured Auto Scaling brings multidimensional benefits. First and foremost, it drastically reduces costs by eliminating wasted resources - you only pay for what you actually need at any given time. For applications with irregular load, savings can reach 60-70% compared to static infrastructure. In addition, Auto Scaling automatically replaces malfunctioning instances, increasing application reliability. It’s worth noting that Auto Scaling is not a solution just for EC2 - you can also use it for other services, such as Amazon ECS, Amazon EKS, DynamoDB or Aurora.

To maximize the benefits of Auto Scaling, there are a few best practices to keep in mind. First, choose scaling metrics carefully - CPU utilization is popular, but for many applications, a better indicator may be the number of connections, queue length or latency. Second, setting appropriate cooldown periods after scaling actions prevents oscillations and unnecessary infrastructure changes. Third, combining Auto Scaling with Spot instances can bring additional savings, especially for workloads that are not sensitive to timeouts. Finally, regularly analyze the history of Auto Scaling actions and adjust the configuration to find the optimal balance between performance, reliability and cost.

Auto Scaling - dynamic infrastructure, dynamic savings

• Pay only for what you use: Automatically adjust the number of instances according to current demand

• Get the right policy: Target tracking for simple cases, step scaling for complex scenarios

• Choose the right metrics: Don’t limit yourself to the CPU - consider application-specific metrics

• Test and optimize: Regularly analyze behavior and adjust parameters for the perfect balance of cost and performance

How to properly size an EC2 instance to meet the needs of the application?

Right-sizing an EC2 instance (aka right-sizing) is the process of matching computing resources to the actual needs of the application - not too big, not too small, but exactly what is needed. This is one of the most fundamental practices of AWS cost optimization, which can result in immediate savings without negatively impacting performance. Instances that are too large lead to wasted resources and unnecessary costs, while instances that are too small can affect application performance and user experience.

The right-sizing process should begin with a thorough analysis of the application’s resource usage. AWS offers several tools that can help with this task: CloudWatch provides detailed metrics on CPU, memory, network and I/O usage, Trusted Advisor identifies potentially suboptimal instances, and Cost Explorer with Rightsizing Recommendations automatically generates suggestions for changes. Third-party tools that can provide more advanced analysis and recommendations are also worth considering. The key is to collect data over a long enough period - a minimum of 2 weeks, and ideally a month or more - to capture all load patterns, including peaks associated with specific days or times.

When selecting a new instance size, it is important to consider not only current resource usage, but also projected future demand, security margins and application characteristics. AWS offers a huge variety of instance families, optimized for specific use cases - from general-purpose instances (e.g., T-series for workloads with variable CPU utilization, M-series for balanced workloads), to instances optimized for compute (C-series), memory (R-series), storage (I-series), to GPU-accelerated (G-series) and FPGA-accelerated (F-series) instances.

It is worth remembering that right-sizing is not a one-time activity, but an ongoing process. As applications evolve, workloads change and new features are introduced, the optimal instance configuration also changes. It is advisable to review and adjust the instance size regularly - for production environments at least quarterly, and for test and development environments even more frequently. This is especially important after significant changes in application or traffic patterns. Consistent use of right-sizing practices can save 30-50% on EC2 costs, which for larger deployments translates into thousands or even tens of thousands per month.

How to optimize data storage costs in S3 and EBS?

Optimizing storage costs on AWS starts with understanding the different storage classes and matching them to data characteristics. In the case of Amazon S3, AWS offers several classes of storage that vary in price, availability and access fees. S3 Standard provides high availability and performance at the highest cost, S3 Intelligent-Tiering automatically moves data between tiers based on access patterns, S3 Standard-IA (Infrequent Access) and S3 One Zone-IA are cheaper but with retrieval fees, and Glacier and Glacier Deep Archive offer the lowest cost for archival data with longer retrieval times.

A key S3 optimization strategy is to implement lifecycle policies that automatically move data between storage classes or delete it after a certain period of time. For example, you can configure a rule that moves data to S3 Standard-IA after 30 days of creation, then to Glacier after 90 days, and finally deletes it after 1 year. This approach can significantly reduce costs with minimal administrative effort. Additionally, consider using S3 Intelligent-Tiering for data with unpredictable access patterns - this class automatically optimizes costs by moving objects between layers based on their usage.

With Amazon EBS (Elastic Block Store), optimization begins with choosing the right type of volume. AWS offers several options that vary in performance and price: General Purpose SSD (gp2/gp3) for most workloads, Provisioned IOPS SSD (io1/io2) for applications requiring high I/O performance, Throughput Optimized HDD (st1) for streaming workloads and Cold HDD (sc1) for infrequently used data. The gp3 volume, introduced recently, offers better performance and lower pricing than gp2, making it an attractive option for many use cases.

Best practices for optimizing storage costs include regularly removing unused resources. Unused EBS volumes, old snapshots and outdated versions of S3 objects generate unnecessary costs. It is worth implementing automation that identifies and removes unneeded resources or notifies administrators of potential candidates for removal. For EBS volumes that must remain active, consider modifying their size or type based on actual usage. For S3, activate S3 Analytics, which provides recommendations for the optimal storage class based on access patterns. These proactive approaches can significantly reduce storage costs, which often account for a significant portion of AWS spending.

Optimization of data storage - a multi-level strategy

• Tailor the class to fit your needs: Use less expensive classes for less frequently used data

• Automate with lifecycle: Configure policies to automatically move and delete data

• Eliminate unnecessary resources: Regularly delete unused volumes, snapshots and old versions of objects

• Analyze and adjust: Use AWS tools to identify optimization opportunities

Are Savings Plans better than Reserved Instances?

Savings Plans, introduced by AWS in 2019, are a flexible alternative to Reserved Instances, offering similar savings with fewer restrictions. The primary difference is that Savings Plans are based on a commitment to a specific level of hourly spending (in dollars), rather than to specific instances or configurations. This fundamental change gives users much more freedom to adapt their infrastructure to changing needs, without losing the discounts that come with a long-term commitment.

AWS offers two main types of Savings Plans: Compute Savings Plans, which provide the most flexibility, covering EC2, Fargate and Lambda regardless of instance family, size, operating system or region, and EC2 Instance Savings Plans, which are limited to a specific instance family in a specific region, but offer slightly higher discounts. As with Reserved Instances, Savings Plans are available with a 1-year or 3-year commitment period and various payment options (upfront, partial upfront payment, no upfront payment), which affects the amount of discount received.

The answer to the question of whether Savings Plans are better than Reserved Instances depends on the specific needs of the organization. Savings Plans tend to work better in dynamic environments that evolve frequently, use a variety of instance types, or regularly migrate between instance families as newer generations emerge. They are also ideal for organizations that use containerization (Fargate) and serverless architecture (Lambda). Reserved Instances, on the other hand, may be a better choice in highly stable environments with predictable, long-term demand for specific instance types.

Management and accounting aspects are also worth considering when making a decision. Savings Plans tend to be easier to manage because they automatically apply to eligible resources without manual adjustments. On the other hand, Reserved Instances offer greater precision in assigning costs to specific teams or projects, which can be important from an internal billing perspective. In practice, many organizations are opting for a hybrid approach, using Reserved Instances for stable baseline infrastructure and Savings Plans for more variable or newer services, thus maximizing flexibility while maintaining significant savings.

How to identify and remove unused resources in AWS?

Identifying and removing unused resources is one of the fastest and easiest methods of optimizing AWS costs, often with immediate results. Unused resources can take many forms: idle EC2 instances, unused EBS volumes, old snapshots, duplicated AMIs, empty S3 buckets, or inactive databases. Each of these items generates costs, even though they bring no business value. Regular “cleaning” of the AWS environment should be standard practice in any organization using the cloud.

AWS provides several native tools to help identify potential waste. AWS Trusted Advisor offers automated recommendations for unused or underutilized resources. AWS Cost Explorer allows you to analyze spending and identify trends or anomalies that may indicate unnecessary resources. AWS Config allows you to create rules that automatically detect non-compliant policies or potentially underutilized resources. For more advanced needs, you can consider third-party tools that offer deeper analysis and automate the process of identifying and removing unnecessary resources.

To effectively manage unused resources, it’s a good idea to implement a systematic approach. First, define clear criteria that determine when a resource is considered unused (e.g., an EC2 instance with less than 1% CPU utilization for 30 days). Second, implement automatic tagging of all resources with information on owner, project and expiration date. Third, establish a regular resource review cycle, such as weekly for development environments and monthly for production. Fourth, implement automation that can temporarily disable suspicious resources (instead of deleting them outright) to give you the ability to respond in case of false alarms.

Special attention should be paid to often overlooked resource types that can generate significant costs: elastic IP addresses that are not assigned to running instances, old EBS snapshots that are no longer needed for data recovery, unused load balancers or inactive API Gateways. For each AWS resource type, specific criteria and automations can be defined to help keep the environment clean. Consistently applied “good housekeeping” practices can lead to savings of 10-20% of total AWS costs, which for larger organizations translates into significant amounts.

Elimination of unnecessary resources - quick savings

• Define criteria: Set clear rules for identifying unused resources

• Automate detection: Use AWS tools and automation to regularly scan your environment

• Implement tagging: Tag resources with owner and expiration date information

• Safe approach: keep resources first, then delete - allow time to react if you make a mistake

Why is asset tagging crucial to cost control?

AWS resource tagging is much more than simple categorization - it’s the foundation of effective cloud cost management. Tags are metadata in the form of key-value pairs that can be assigned to almost any AWS resource, from EC2 instances and EBS volumes to S3 buckets and databases. A well-designed tagging strategy enables detailed cost analysis by different business dimensions, such as teams, projects, environments, applications or cost centers. Without proper tagging, the AWS account remains a black box, making it impossible to understand exactly what is generating costs and who is responsible for them.

Implementing an effective tagging strategy requires careful planning. Initially, define a set of mandatory tags that must be assigned to each resource - typical examples include Name (descriptive name of the resource), Environment (prod, dev, test), Project/Application, Owner (team or responsible person) and CostCenter (unit of account). It’s also worth considering lifecycle-related tags, such as CreationDate or ExpiryDate, which make it easier to identify temporary resources. Once standards are defined, the key is to implement enforcement mechanisms - you can use AWS Organizations and Service Control Policies (SCP) to enforce tagging when creating resources, or AWS Config to identify resources without the required tags.

To realize the full potential of tagging in the context of cost control, it is necessary to activate Cost Allocation Tags in the Billing and Cost Management console. This is a critical step that enables tags to be used in cost reports and analytical tools such as Cost Explorer. Keep in mind that only activated tags are visible in cost reports, and their activation is not retroactive - only costs generated after activation will be visible. Therefore, it is a good idea to implement a tagging strategy and activate cost allocation tags as early as possible.

Beyond basic analysis, tagging opens the door to advanced cost management practices. You can create custom cost reports filtered by any combination of tags, define budgets for specific teams or projects based on tags, and even implement automation that responds to unexpected cost spikes in specific infrastructure segments. In addition, tags are essential when implementing an internal billing (chargeback) model, where AWS costs are assigned to the appropriate business units or departments. In organizations with multiple teams using AWS, this approach promotes cost accountability and expense awareness at all levels.

How to use AWS Trusted Advisor for automatic optimization?

AWS Trusted Advisor is a powerful monitoring tool that continuously analyzes your AWS environment and provides personalized recommendations in five key areas: cost optimization, performance, security, reliability and service limits. From a cost optimization perspective, Trusted Advisor is an invaluable ally, identifying specific savings opportunities without having to manually analyze your entire infrastructure. The tool works with AWS best practices and usage patterns of thousands of customers, allowing it to detect common inefficiencies and waste.

In the cost optimization category, Trusted Advisor performs a series of automated checks that include, but are not limited to: underutilized or underutilized EC2 instances, suboptimal allocated EBS bandwidth, unused EBS volumes, unassigned Elastic IPs, instances that could benefit from Reserved Instances or Savings Plans, suboptimal S3 storage classes, or inefficient load balancer configurations. Each recommendation includes a detailed description of the problem, a list of resources that need attention, and an estimated savings potential. It is almost a ready-made to-do list for the optimization team.

To get the most out of Trusted Advisor, it’s a good idea to regularly review its recommendations and implement suggested changes. You can do this manually using the AWS console, or automate the process using AWS CloudWatch and AWS Lambda. For example, you can set up automatic email or SMS notifications when new recommendations are made, or create Lambda functions that automatically perform specific optimization actions, such as stopping unused instances or changing the storage class of unused S3 objects. This approach allows for continuous optimization without the need to manually check recommendations on a regular basis.

It is worth noting that access to the full functionality of Trusted Advisor depends on the AWS support plan you have. A basic AWS account gives access to only six controls, while the Business, Enterprise On-Ramp and Enterprise plans unlock the full range of more than 50 controls. Given the potential savings, the investment in a higher support plan often pays for itself quickly. For organizations with limited budgets, an alternative may be to use AWS Cost Explorer and AWS Config, which offer partially overlapping functionality, though without as comprehensive an approach as Trusted Advisor.

Trusted Advisor - your automatic optimizer

• Comprehensive controls: Automatically identifies cost inefficiencies throughout the environment

• Specific recommendations: Provides detailed suggestions with estimates of potential savings

• Automation capabilities: Integrates with CloudWatch and Lambda for automated optimization implementation

• Continuous monitoring: constantly analyzes the environment, identifying new opportunities as the infrastructure evolves

When is it worth migrating workloads to a serverless (Lambda) architecture?

Migrating to a serverless architecture, particularly AWS Lambda, can yield significant cost savings in the right scenarios. The fundamental difference between the traditional server-based model (EC2) and Lambda is the billing model - with EC2, you pay for instances reserved or running, regardless of actual usage, while Lambda only bills for actual code executed, to the nearest millisecond. This difference makes Lambda particularly attractive cost-wise for workloads with irregular or intermittent usage, where traditional servers would be inefficiently utilized most of the time.

Ideal candidates for migration to Lambda are applications characterized by sporadic usage patterns: processing cyclic jobs (e.g., nightly jobs), systems that process events (e.g., responding to changes in S3 or DynamoDB), microservices with low or variable load, or systems that process data in batches. Lambda is also perfect for cases where scalability is key - it automatically adapts to the load, from single requests to thousands of concurrent calls, without the need for manual infrastructure management. As a result, you not only save on infrastructure costs, but also reduce the operational burden on your DevOps team.

However, the decision to migrate to Lambda should be preceded by a careful analysis of application characteristics. Lambda has some limitations and not all workloads are suitable candidates. Applications requiring long processing times (more than 15 minutes), systems with very high performance requirements (e.g., low latency), or applications with high memory requirements (more than 10 GB) may not be good candidates. In addition, monolithic applications may require significant refactoring to run effectively in a serverless model, which increases the initial cost of migration.

In practice, many organizations are opting for a hybrid approach, migrating to Lambda those components that will benefit most from the serverless model, while keeping more permanent workloads on EC2, potentially using Reserved Instances or Savings Plans. It is recommended to start with a pilot migration of non-critical, well-isolated components to gain experience and better understand the cost and operational implications of a serverless architecture in a specific environment. Such an incremental migration minimizes risk and allows for empirical verification of benefits before deciding on a broader transformation.

How to reduce the cost of transferring data between AWS regions?

Data transfer costs in AWS are often a hidden pitfall in billing, especially in distributed, multi-regional architectures. Unlike other services, which show a downward trend over time, data transfer prices remain relatively high. For example, the cost of transferring 1 TB of data from the eu-west-1 region (Ireland) to eu-central-1 (Frankfurt) is about $20, and transferring the same volume to Asia can cost as much as $90. In multi-regional applications processing large volumes of data, these costs can quickly escalate to tens of thousands of dollars per month.

Analysis of current transfer costs in AWS (2025)

The table below shows current data transfer costs for popular scenarios:

Type of transfer	Cost (approximate)	Comments
Coming to AWS	$0.00	Transfer to AWS is always free
Between zones (AZs) in the same region	$0.01/GB	Often overlooked in cost analyses
Between regions in the same continent	$0.02/GB	E.g. eu-west-1 to eu-central-1
Between regions on different continents	$0.05-0.09/GB	Depending on the regions
From AWS to the Internet (first 10TB/month)	$0.05-0.09/GB	Depends on the region
From CloudFront to the Internet	From $0.02/GB	Significantly lower than directly from AWS services

Strategies to optimize transfer costs

1. mapping and visualizing data flows

The first step is to thoroughly understand the data flows in your architecture. Here is a sample mapping methodology:

2. architectural approaches to reducing transfer costs

The most effective strategies are architectural in nature:

A. Location of related resources

B. Implementation of local caching

C. Optimization of replication frequency

For data synchronization between regions, instead of continuous replication, consider a batch approach during periods of lower load:

3. advanced technical strategies and the latest AWS services

A. AWS CloudFront as a proxy for inter-regional transfer

Newer AWS CloudFront deployments can serve not only as a CDN, but also as a transfer cost optimizer:

B. AWS PrivateLink and Transit Gateway

For complex multi-region topologies, AWS PrivateLink and Transit Gateway offer price-optimized communication mechanisms:

C. Use of AWS Global Accelerator

The Global Accelerator (Economy Mode) savings mode, introduced in 2023, reduces transfer costs by 25-40% for global deployments while retaining most of the performance benefits.

4. third-party tools to support optimization

• Datadog Network Performance Monitoring - provides detailed visibility into data flows and associated costs

• Spot.io Data Transfer Cost Analyzer - automatically identifies costly data flows and suggests optimizations

• CloudZero - offers advanced data transfer cost analytics with architecture recommendations

Data transfer optimization - reducing hidden costs

• Regional consolidation: group related resources in the same region to minimize costly transfers

• Use Direct Connect: Consider a dedicated connection for inter-regional transfers at high volumes

• Optimized services: Use AWS native replication mechanisms instead of manual transfers

• Data Compression: Reduce the volume of transferred data by compressing and optimizing formats

• Strategic caching: Implement caching solutions that minimize repetitive data transfers

Why are regular infrastructure audits a must?

Regular audits of AWS infrastructure are a fundamental part of a long-term cost optimization strategy. Even the best-designed environments become entropic over time - temporary resources appear that are never removed, application usage patterns change, undocumented dependencies emerge, and incremental modifications can lead to inefficient configurations. Without systematic reviews, these minor inefficiencies accumulate, leading to significant waste of resources and unnecessary expenses.

An effective audit should include a comprehensive review of all aspects of your AWS infrastructure. Start with an inventory of all resources, verifying that they are still needed and properly sized. Verify that all resources are properly tagged according to your tagging strategy, which is critical for accurate cost allocation. Analyze usage patterns, identifying potential candidates for Reserved Instances or Savings Plans. Investigate whether the architecture uses the right AWS services for specific tasks - for example, whether some EC2 workloads could be more cost-effective in a serverless model.

In addition to technical aspects, the audit should also cover operational processes and practices. Verify that procedures are in place to manage the asset lifecycle - from creation to monitoring to retirement. Verify that teams are following cost optimization guidelines, and that mechanisms are in place to enforce these policies. Evaluate whether current AWS budgeting and cost accounting processes are effective and promote financial accountability among technical teams.

To be truly effective, audits should be regular and systematic. For production environments, a comprehensive audit is recommended at least quarterly, with more frequent, more targeted reviews of specific areas. It’s also worth considering automating parts of the audit process - tools such as AWS Config, Trusted Advisor or third-party solutions can significantly reduce manual effort and provide more consistent results. After each audit, it’s crucial to develop a concrete action plan, with clearly defined tasks, responsibilities and deadlines, so that identified optimization opportunities are actually implemented.

How to plan a budget using AWS Budgets?

AWS Budgets is a tool that is a key component of proactive expense management, offering functionality far beyond simple expense tracking. Unlike AWS Cost Explorer, which focuses on historical analysis, Budgets allows you to plan future expenses, set limits and automate responses to overruns. According to AWS data, organizations actively using Budgets achieve on average 15-20% better predictability and cost control.

Creating effective AWS budgets requires a layered approach. Here is a sample budgeting framework:

• Organization-level budgets - monitoring total AWS spending:

• Budgets per service - monitoring key cost generators:

• Specialized budgets - monitoring specific aspects of use:

Here are the specific steps of implementing AWS Budgets:

• Configure the cost budget with the Forecast option:

• Budget Actions implementation for automatic response:

Typical pitfalls when implementing AWS Budgets:

• The trap of too many alerts - setting too many alerts with low thresholds leads to “alert fatigue” and ignoring notifications. It is better to start with 3-5 key budgets and gradually expand.

• The Rigid Limits Trap - Setting rigid limits without considering seasonality and natural business growth leads to false alarms. Use Planned Budgets that take into account expected fluctuations.

• The trap of over-automation - overly aggressive budget actions (such as blocking all services) can affect business continuity. Use a graded approach: first notifications, then limited actions, and finally more drastic measures.

In addition to native AWS Budgets, consider complementary tools:

• CloudFix - automatically identifies and implements cost optimizations with minimal risk

• ProsperOps - optimizes booking portfolio and Savings Plans using AI algorithms

• Kubecost - for Kubernetes environments, provides detailed per sub/deployment cost visibility

In 2023-2025, AWS made significant improvements to Budgets:

• Budgets Insights - automatically identifies unusual spending patterns

• Integrated Anomaly Detection - detects spending anomalies and suggests corrective actions

• Multi-dimension budgets - allows you to create budgets that take into account multiple dimensions simultaneously

A comprehensive budgeting strategy in AWS

An effective budgeting strategy is more than just a tool - it is a process that combines people, tools and procedures:

• Monthly review meetings - analysis of budgets vs actual expenses

• Quarterly adjustments to budgets - adjustment based on trends and business plans

• Annual strategic planning - setting long-term spending targets

AWS Budgets - proactive expense management

• Get ahead of problems: Set alerts for projected overruns, not just actual expenses

• Segment budgets: Create separate budgets for different teams, projects and environments

• Automate responses: Configure budget actions that automatically respond to overruns

• Review regularly: Adjust budget limits as infrastructure and business needs evolve

• Use tiered budgets: combine organizational, specialized and per-service budgets

What is FinOps and how to implement this methodology in AWS?

FinOps (Financial Operations) is an evolving field of operations management that combines financial, technological and business expertise to deliver maximum value from cloud investments. In the context of AWS, FinOps goes far beyond simple technical optimizations - it’s a comprehensive approach to managing the economics of the cloud. According to the FinOps Foundation, organizations using mature FinOps practices achieve an average of 20-35% savings compared to organizations that focus solely on the technical aspects of optimization.

AWS’s FinOps maturity model includes three key phases that are not linear, but rather form a continuous cycle of improvement:

Phase 1: Inform

In this phase, the fundamental objective is to provide visibility of costs and assign them to the right teams/projects. Key activities include:

• Implement a consistent tagging strategy:

• Implementation of cost dashboards for different levels of the organization:

Detail dashboards for engineering teams with cost per service/instance

• Mid-level dashboards for managers with trends and cost forecasts

• High-level dashboards for executives showing key metrics and ROIs

• Reporting automation using:

AWS Cost and Usage Report as a data source

• AWS Glue for ETL and data transformation
• Amazon QuickSight or external tools like Tableau/PowerBI for visualization

The most common pitfall in this phase is the “data without insight” syndrome - generating huge amounts of cost data without business context. Linking costs to business metrics (e.g., cost per transaction, cost per customer) is critical.

Phase 2: Optimize.

In this phase, teams actively optimize costs based on the data from Phase 1. Key activities include:

• Unit-cost metrics program - define and track business-specific unit costs:

• Implementation of governance processes:

Regular cost reviews with engineering and business teams

• Approval policies for new resources above certain thresholds

• Automatic tagging and policy enforcement with AWS Organizations

• Implementation of the chargeback/showback model:

Actual charging of AWS costs (chargeback) to teams/projects

• Or showing costs without actual budget allocation (showback)

A typical pitfall at this stage is over-engineering optimization without considering total cost of ownership (TCO) and business value. For example, overly aggressive right-sizing can jeopardize application scalability during unexpected traffic spikes.

Phase 3: Operate.

In this advanced phase, FinOps becomes an integral part of the organizational culture. Key activities include:

• Automation of optimization decisions:

• Predictive cost modeling - using ML to forecast costs and identify anomalies:

Amazon SageMaker to build predictive models

• AWS Cost Anomaly Detection to automatically detect deviations
• Advanced decentralized governance:

Budgets controlled by engineering teams

• Cost-effectiveness KPIs as part of performance evaluation of teams
• FinOps architects embedded in product teams

External tools that specifically support mature FinOps practices:

• Apptio Cloudability - advanced cost modeling and analytics

• **CloudHealth by ** - a comprehensive cloud cost management platform

• Densify - ML-based resource optimization

• Ternary - advanced chargeback and cost analysis capabilities

For organizations just starting to implement FinOps, the following 90-day plan is recommended:

• Days 1-30: Establish cost visibility, basic tagging, first dashboards

• Days 31-60: Implement FinOps pilot in one team, with focus on quick-wins

• Days 61-90: Formalize FinOps Center of Excellence, expand to more teams

Critical success factors include C-level sponsorship, clearly defined roles and responsibilities, and a balance between cost optimization and innovation - an overly restrictive approach to cost can stifle innovation, which is critical to long-term success in the cloud.

When is it a good idea to engage outside experts for optimization?

The decision to engage external experts to optimize AWS costs should be based on an analysis of the potential return on investment (ROI). In practice, the involvement of external specialists is justified in several specific scenarios.

Scenarios justifying the involvement of experts

1. AWS spending threshold exceeds break-even point

There is a certain threshold of spending on AWS, above which it always pays to engage external experts. According to 2024 industry analysis, this break-even point is:

AWS monthly expenses	Potential savings	Typical cost of experts	ROI (6 months)
< $5,000	10-15% ($500-750)	$3,000-5,000	Negative
$5,000 - $20,000	15-25% ($750-5,000)	$5,000-10,000	Neutral/Positive
$20,000 - $100,000	20-35% ($4,000-35,000)	$10,000-20,000	Definitely positive
> $100,000	25-40% (>$25,000)	$20,000+	Highly positive

Example of ROI calculation for a company spending $50,000 per month:

2 Characteristics of the AWS environment

The need for outside expertise often arises from the characteristics of the cloud environment:

(a) Architecture complexity

Such a complex structure requires expertise for effective optimization, especially in the areas of reservation management, data flows and resource sharing.

(b) Advanced Environmental Services using specialized services such as:

• AWS Lambda with thousands of features

• Amazon EKS with hundreds of containers

• AWS Step Functions for Complex Workflows

• Amazon SageMaker for machine learning

These advanced components have non-intuitive pricing models and require specific optimization strategies that external experts are familiar with from many implementations.

(c) Hybrid/multi-cloud deployments

Optimizing such environments requires an understanding of cost and performance in different configurations, a rare in-house competency.

Indicators of the need for external assistance

There are also specific indicators that suggest your organization could benefit from expert assistance:

• Reservation utilization rate below 85% - indicates ineffective commitment management

• The cost of resources not allocated to the project/team exceeds 15% of the total expenditure

• The ratio of the cost of non-productive to productive environments exceeds 40%

• Monthly cost fluctuations exceed 25% with no correlation to business metrics

How to choose the right partner

When choosing a partner for AWS cost optimization, it’s worth focusing on a few key aspects:

• Certifications and certifications

AWS Premier Consulting Partner status

• Specialization in Cloud Financial Management or AWS Well-Architected

• Minimum of 3 certified AWS experts (Professional level)

• Industry experience

• Collaboration and billing model The most effective billing models are:

Success fee (% on actual realized savings)

• Fixed fee + success fee

• Subscription for continuous management and optimization

• Knowledge Transfer Approach Make sure the partner offers:

Documentation of the implemented changes

• Training for your teams

• Documentation of optimization processes

• Access to tools used during optimization

New trends in AWS external optimization (2025)

The AWS optimization services market is evolving, introducing new collaboration models:

• Cloud FinOps as a Service (CFaaS) - instead of one-time audits, companies offer ongoing cost management as a subscription service

• AI-driven optimization - using ML algorithms to predict usage patterns and automatically optimize resources

• Hybrid expertise teams - models that combine external experts with internal teams into integrated FinOps teams

Working with experts - maximizing savings

Success fee models: top partners offer compensation based on actual savings achieved

Expertise**:** Experts know the nuances of pricing and architecture that lead to the highest savings

Objective view: External consultants provide a fresh perspective, free from internal biases

Experience from multiple deployments: AWS partners use practices proven with hundreds of customers

ROI: When spending heavily on AWS, the cost of experts pays for itself many times over in identified savings

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