3.4.4. Cost Optimization for Serverless & Managed Services

First Principle: Optimizing costs for serverless and managed services involves writing efficient code, judiciously configuring resource allocations, and understanding pay-per-use billing models, ensuring financial efficiency throughout the application lifecycle.

For developers, managing costs for serverless and managed services differs from traditional instance-based pricing. It's about optimizing what you actually consume rather than managing fixed hourly costs.

Key Strategies for Serverless & Managed Service Cost Optimization:

• AWS Lambda Cost Optimization:
  • Memory Allocation: Configure the optimal memory for your Lambda function. Since CPU is proportional to memory, finding the sweet spot between faster execution (higher memory) and lower GB-second billing is key.
  • Execution Duration: Optimize your code to complete its task as quickly as possible to minimize duration.
  • Provisioned Concurrency: For latency-sensitive functions, use provisioned concurrency to avoid cold starts, but be aware it incurs cost even when idle.
• Amazon DynamoDB Cost Optimization:
  • On-Demand Capacity: For unpredictable workloads, pay only for actual reads/writes, avoiding over-provisioning of RCUs/WCUs.
  • Provisioned Capacity with Auto Scaling: For predictable workloads, optimize provisioned capacity and use Auto Scaling to adjust.
  • Minimize Large Items/Attributes: DynamoDB bills based on item size in 4KB chunks for reads and 1KB chunks for writes. Optimize item sizes.
• Amazon S3 Cost Optimization:
  • S3 Storage Classes: Choose the right class based on access frequency (Standard, Standard-IA, Glacier).
  • S3 Lifecycle Policies: Automate tiering to cheaper storage as data ages.
• Reduce Data Transfer Out: Design applications to minimize data egress from AWS Regions.

Scenario: You've deployed a serverless application using Lambda functions and a DynamoDB table. The AWS bill for these services is higher than expected. You observe that your Lambda functions have high execution durations, and your DynamoDB table has inconsistent traffic.

⚠️ Exam Trap: Lambda costs = (number of requests × duration × memory). Reducing duration by increasing memory can LOWER total cost even though per-ms price is higher. Always test: more memory → faster execution → lower bill.