Oversized Lambda Functions

Why This Matters

Lambda pricing is based on memory allocation and execution duration, not actual usage. Over-allocating memory means paying for compute you're not using. Many functions are conservatively sized during development but never optimized for production workloads.

Common causes:

• Conservative memory allocation without performance testing
• Functions that started small but grew in complexity
• Over-provisioning to avoid cold start issues
• Legacy functions not optimized for current workloads

How to Identify Oversized Functions

AWS Saver flags Lambda functions as over-provisioned when they meet these criteria:

1. Timeout utilization under 20% (using under 20% of allocated execution time)
2. Memory allocation 1024MB or higher (high-memory functions worth optimizing)
3. Above 1000 invocations monthly (statistical significance)
4. Monthly cost above $10 (meaningful savings potential)
5. Functions running above 10 seconds are flagged for potential container migration

How to Fix Oversized Lambda Functions

Step 1: List all Lambda functions with memory and timeout

aws lambda list-functions \
  --query 'Functions[].{Name:FunctionName,Memory:MemorySize,Timeout:Timeout}'

Step 2: Get detailed function configuration

aws lambda get-function-configuration --function-name your-function-name

Step 3: Check invocation count over 30 days

aws cloudwatch get-metric-statistics \
  --namespace AWS/Lambda \
  --metric-name Invocations \
  --dimensions Name=FunctionName,Value=your-function-name \
  --statistics Sum \
  --start-time $(date -u -d '30 days ago' +%Y-%m-%dT%H:%M:%SZ) \
  --end-time $(date -u +%Y-%m-%dT%H:%M:%SZ) \
  --period 86400

Step 4: Get average duration metrics

aws cloudwatch get-metric-statistics \
  --namespace AWS/Lambda \
  --metric-name Duration \
  --dimensions Name=FunctionName,Value=your-function-name \
  --statistics Average \
  --start-time $(date -u -d '30 days ago' +%Y-%m-%dT%H:%M:%SZ) \
  --end-time $(date -u +%Y-%m-%dT%H:%M:%SZ) \
  --period 86400

Step 5: Update function memory allocation

aws lambda update-function-configuration \
  --function-name your-function-name \
  --memory-size 512

Prevention Tips

Start with minimal memory: Begin with 128MB and scale up based on actual performance needs, not estimates.

Use Lambda Power Tuning: Leverage AWS Lambda Power Tuning tool to find optimal memory/cost balance.

Monitor regularly: Set up CloudWatch alarms for duration vs timeout ratio to catch over-provisioning.

Consider Graviton2: Use ARM-based processors (Graviton2) for up to 34% better price-performance.

Automation Available

Skip the manual work. AWS Saver automatically analyzes Lambda functions using the same timeout and memory thresholds.

✅ Timeout utilization analysis - Identifies functions using less than 20% of allocated execution time

✅ Memory optimization detection - Flags over-provisioned functions with 512MB+ memory

✅ Cost impact calculation - Shows potential monthly savings from rightsizing

✅ Long-running function alerts - Recommends container alternatives for functions over 10s