1. Pricing
1.1. On-Demand
1.1.1. recommend that you use On-Demand Instances for applications with short-term, irregular workloads that cannot be interrupted.
1.2. Reserved instances
1.2.1. Amazon EC2 Reserved Instances (RI) provide a significant discount (up to 72%) compared to On-Demand pricing and provide a capacity reservation when used in a specific Availability Zone.
1.2.2. Why Use?
1.2.2.1. Save Money and Maintain Flexibility
1.2.2.2. Reserve Capacity
1.2.3. Amazon EC2 RI Types?
1.2.3.1. Standard RIs: These provide the most significant discount (up to 72% off On-Demand) and are best suited for steady-state usage.
1.2.3.2. Convertible RIs: These provide a discount (up to 54% off On-Demand). Like Standard RIs, Convertible RIs are best suited for steady-state usage.
1.2.3.3. Scheduled RIs: These are available to launch within the time windows you reserve.
1.3. Spot instances
1.3.1. Amazon EC2 Spot Instances let you take advantage of unused EC2 capacity in the AWS cloud. Spot Instances are available at up to a 90% discount compared to On-Demand prices.
1.3.2. Why use?
1.3.2.1. Low, predictable price
1.3.2.2. Massive scale
1.3.2.3. Easy to use
1.4. Saving Plans
1.4.1. Savings Plans is a flexible pricing model offering lower prices compared to On-Demand pricing, in exchange for a specific usage commitment (measured in $/hour) for a one- or three-year period.
1.4.2. Types of Savings Plans
1.4.2.1. Compute Savings Plans
1.4.2.2. EC2 Instance Savings Plans
1.4.2.3. Amazon SageMaker Savings Plans
1.4.3. Why use?
1.4.3.1. Flexible plans
1.4.3.2. Significant savings
1.4.3.3. Easy to use
1.5. Dedicated Hosts
2. Auto Scaling
3. Instance Types
3.1. General Purpose
3.1.1. The most popular; used for web servers, development environments, etc.
3.2. Compute Optimized
3.2.1. Good for compute-intensive applications such as some scientific modeling or high-performance web servers.
3.3. Memory Optimized
3.3.1. Used for anything that needs memory-intensive applications, such as real-time big data analytics, or running Hadoop or Spark.
3.4. Accelerated Computing
3.4.1. Include additional hardware (GPUs, FPGAs) to provide massive amounts of parallel processing for tasks such as graphics processing.
3.5. Storage Optimized
3.5.1. Ideal for tasks that require huge amounts of storage, specifically with sequential read-writes, such as log processing.