Serverless Cost Optimization: Lambda and DynamoDB

Serverless Isn't Free: Optimizing Lambda and DynamoDB Costs

Serverless architectures eliminate server management, but costs can still spiral if you're not careful. Lambda's per-invocation pricing and DynamoDB's capacity units require different optimization strategies than traditional EC2 workloads.

Lambda Cost Optimization

1. Right-Size Memory (and CPU)

Lambda allocates CPU proportionally to memory. A 128 MB function gets half the CPU of a 256 MB function. Sometimes increasing memory makes functions run faster and cheaper overall.

Use AWS Lambda Power Tuning (an open-source Step Functions workflow) to find the optimal memory setting for each function.

2. Reduce Invocation Count

• Batch SQS messages — increase BatchSize to process 10-100 records per invocation instead of 1
• Use EventBridge batching and filtering to reduce unnecessary triggers
• Cache responses with API Gateway caching to avoid invoking Lambda for repeated requests

3. Optimize Execution Duration

• Use connection pooling (reuse database connections across invocations)
• Minimize cold starts with Provisioned Concurrency for latency-sensitive functions
• Use Lambda Layers to share common dependencies and reduce package size

DynamoDB Cost Optimization

On-Demand vs Provisioned Capacity

Key DynamoDB Savings

1. Switch to Provisioned + Auto Scaling for tables with predictable patterns
2. Enable DynamoDB TTL to automatically delete expired items (free)
3. Use projected attributes in GSIs to reduce storage and read costs
4. Enable DynamoDB Accelerator (DAX) for read-heavy workloads to reduce consumed RCUs

Eazy SaaS Tip: For Lambda, run Power Tuning on your top 10 functions by invocation count. For DynamoDB, switch steady-state tables from On-Demand to Provisioned + Auto Scaling — it typically saves 50-70%.