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Difficulty: MediumCost-Optimized Compute Selection and Purchasing Strategies

An IoT smart home company is deploying its application platform to AWS. The platform consists of two main workloads:

1. A backend microservice API that handles device telemetry updates. The traffic is highly unpredictable and spiky, with individual request durations averaging less than 300 ms300\text{ ms}.
2. A background data processing engine that runs continuously 2424 hours a day, 77 days a week (24/724/7) on Amazon Elastic Container Service (Amazon ECS) using the AWS Fargate launch type.

Which two solutions should a solutions architect recommend to achieve the most cost-effective compute architecture?

  1. Host the backend API on AWS Lambda to leverage automatic scaling and pay-per-request billing.Answer
  2. B
    Purchase an EC2 Instance Savings Plan to lower the cost of the Amazon ECS on AWS Fargate workload.
  3. C
    Migrate the background data processing engine to AWS Lambda to reduce costs through serverless scaling.
  4. Purchase a Compute Savings Plan to reduce the cost of the Amazon ECS on AWS Fargate workload.Answer
  5. E
    Purchase a Compute Savings Plan to reduce the cost of the Amazon ECS on AWS Fargate workload and the write capacity of an Amazon DynamoDB table.

Answer

To optimize costs, host the backend API on AWS Lambda to benefit from automatic scaling and pay-per-request pricing, and purchase a Compute Savings Plan to cover the continuous Amazon ECS on AWS Fargate background workload.
Hosting the backend API on AWS Lambda is correct because it charges only per request and execution duration, which is highly cost-effective for unpredictable, spiky traffic with short execution times. Purchasing a Compute Savings Plan is the correct discount strategy for the Amazon ECS on AWS Fargate workload because Compute Savings Plans apply a discount to Fargate, Lambda, and EC2 compute usage.

Step-by-Step Solution

1
Analyze the backend API requirements.
The API handles spiky, unpredictable traffic with very short request durations (under 300 ms300\text{ ms}).
This workload is a perfect fit for AWS Lambda's pay-per-request billing model, avoiding idle server costs.
2
Analyze the background processing engine requirements.
The engine runs continuously 24/724/7 on ECS Fargate, establishing a highly predictable baseline load.
Because it runs continuously, it requires a pricing discount commitment rather than On-Demand pricing.
3
Evaluate purchasing strategies for Fargate and other services.
A Compute Savings Plan covers Fargate compute costs, whereas EC2 Instance Savings Plans and Compute Savings Plans do not cover non-compute services like DynamoDB.
Compute Savings Plans provide the required discounts for Fargate workloads while retaining flexibility.

Key Concept

Selecting the optimal compute model (Lambda vs. ECS Fargate) and purchasing strategy (Compute Savings Plans) based on traffic patterns and service coverage rules.
Estimated Time:1m 30s
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Cost-Optimized Compute Selection and Purchasing Strategies