This lesson explores the cost benefits of right-sizing an EC2 instance by demonstrating the potential savings from adjusting instance sizes based on resource utilization. It highlights the importance of using tools like AWS Trusted Advisor, CloudWatch, and third-party tools to identify overprovisioned instances and optimize costs by aligning instance types with actual workload needs.

This lesson explains how AWS Compute Optimizer helps you right-size your EC2 instances by analyzing CPU, storage I/O, and network I/O data collected from CloudWatch over a period of 14 days. It provides recommendations to optimize instance usage and save costs, with a step-by-step demonstration of setting up an instance and viewing the generated recommendations for resizing.

This lesson explains how AWS EC2 Reserved Instances can provide substantial cost savings of up to 72% compared to On-Demand pricing by committing to a one- or three-year term. Reserved Instances offer flexible payment options and allow users to maintain predictable, consistent workloads, while also offering Convertible Reserved Instances for more flexibility and Scheduled Reserved Instances for time-specific needs, both providing additional cost benefits over On-Demand pricing.

This lesson explains the differences between regional and zonal AWS EC2 Reserved Instances. Regional Reserved Instances provide discounted rates across multiple availability zones without a capacity reservation and offer instance size flexibility for Linux instances, while Zonal Reserved Instances are specific to one availability zone and guarantee capacity but lack size flexibility.

This lesson explains how AWS Compute Savings Plans provide flexibility and cost savings for EC2, Lambda, and Fargate by allowing users to commit to a specific spending level over a one- or three-year term, offering savings up to 54% without being locked into specific instance types, regions, or operating systems. It also covers EC2 Instance Savings Plans, which provide even greater savings up to 72% by committing to specific instance families, and walks through examples of how these plans can reduce overall costs compared to on-demand pricing.

In this lesson, you'll learn how to save money using AWS Spot Instances by leveraging unused AWS capacity at significant discounts of up to 90% compared to On-Demand pricing. The lesson covers the benefits, such as cost savings for stateless, fault-tolerant workloads, and explains how to request Spot Instances and configure them for specific use cases where instance termination is manageable.

In this lesson, you'll learn about the billing implications of stopping and starting EC2 instances in AWS. While stopping an instance stops the on-demand hourly charges, you may still incur costs for associated resources such as Elastic IPs and EBS volumes, which continue to be billed even when the instance is not running.

In this lesson, you'll learn about the AWS Instance Scheduler, which uses resource tags and a Lambda function to automatically stop and start instances based on a defined schedule, helping to reduce costs significantly. The setup involves configuring services like Lambda, DynamoDB, and CloudWatch, with resources created through a CloudFormation template to manage schedules for EC2 instances across multiple accounts and regions.

In this lesson, we'll see how to set up an Auto Scaling Group (ASG) in the AWS Management Console and highlight potential cost-saving opportunities. Key considerations include right-sizing the EC2 instance types, using appropriate purchasing models (On-Demand or Spot), setting accurate scaling policies, and configuring the desired, minimum, and maximum capacities to avoid unnecessary costs while maintaining optimal performance.

In this lesson, we explore ways to reduce EBS costs by selecting the appropriate EBS volume types and ensuring they are properly sized to avoid paying for unused storage or over-provisioned IOPS. Additionally, we discuss the importance of identifying unattached volumes, using CloudWatch and Lambda for automation, and enabling "Delete on Termination" when creating EC2 instances to prevent unused volumes from incurring charges.

This lesson explores how to effectively manage EBS snapshots for cost savings by using the Amazon Data Lifecycle Manager to automate backup schedules and deletions. Proper tagging of AWS resources is crucial to efficiently organize, automate, and manage lifecycle policies, ensuring that outdated snapshots are removed as needed without manual intervention.

This lesson covers the basics of Amazon S3 pricing, focusing on the different storage classes and how each can impact costs depending on usage patterns. Understanding these pricing models, including S3 Standard, Infrequent Access, One Zone IA, Glacier, and Glacier Deep Archive, helps identify opportunities for cost savings by moving data to the most appropriate and cost-effective storage tier.

This lesson explains how to use S3 Analytics Storage Class Analysis to understand data access patterns in S3 buckets and identify opportunities to transition data to more cost-effective storage classes, like Standard Infrequent Access. By setting up filters and analyzing usage over time, you can determine which objects are infrequently accessed and plan appropriate lifecycle policies for cost optimization.

This lesson covers how to configure S3 lifecycle rules to automate the transition of data between storage classes based on access patterns and to set expiration policies for data retention management. By applying filters, tags, and rules, you can efficiently manage S3 storage costs and ensure compliance with data retention policies without manual intervention.

This lesson covers S3 Intelligent Tiering, a storage class that automatically optimizes costs by moving data between frequent and infrequent access tiers based on usage patterns without requiring manual analysis or lifecycle policies. Intelligent Tiering offers the same storage costs as other S3 tiers, with an additional small monthly monitoring fee, making it ideal for unpredictable access patterns or when avoiding complex data management.

This lesson explores AWS pricing documentation, focusing on understanding the costs associated with VPCs, such as NAT Gateway usage and data transfer fees. It emphasizes the importance of thoroughly reading AWS billing details to avoid unexpected charges, especially when setting up services like VPCs, EC2 instances, and managing data transfers across different regions or availability zones.

This lesson covers the fundamentals of AWS data transfer costs, highlighting scenarios that may lead to increased billing due to data transfer. It uses the AWS Data Transfer Costs diagram to explain various charges associated with services like Direct Connect, EC2 instances, and load balancers, emphasizing the importance of understanding these costs for effective cost management in AWS environments.

This lesson provides quick tips for saving costs in a development or test environment. It suggests avoiding always-on resources like load balancers and NAT gateways and instead creating and deleting these resources daily using CloudFormation, as well as using Instance Scheduler to stop and start EC2 instances to reduce unnecessary billing.

This lesson covers ways to reduce Amazon RDS costs by right-sizing instances, choosing the appropriate database engine, and using reserved instances. It also discusses optimizing instance usage, avoiding unnecessary multi-AZ deployments, understanding instance size flexibility limitations, and being mindful of other associated costs such as storage, backups, and data transfer.

This lesson discusses strategies to reduce DynamoDB costs by choosing the appropriate capacity mode—provisioned or on-demand—based on application needs, optimizing partition keys for even data distribution, and leveraging DynamoDB Accelerator (DAX) for caching. It also covers the use of secondary indexes to optimize complex queries and the importance of understanding associated costs like storage, backups, and global tables.

This lesson explains how caching services like CloudFront and ElastiCache in AWS can enhance application performance and potentially reduce costs. By caching frequently accessed data at geographically distributed edge locations or in-memory caches, CloudFront reduces the load on the origin servers, and ElastiCache speeds up read-intensive database queries, allowing for smaller, less costly infrastructure.