AWS Lambda Optimization: Reducing Costs and Improving Performance
Optimizing Lambda functions reduces costs and improves performance. This guide covers key strategies.
Lambda charges based on memory allocation and execution time:
# Test different memory sizes
# 128MB: $0.0000000021 per ms
# 512MB: $0.0000000083 per ms
# 1024MB: $0.0000000167 per ms
# Find optimal memory size
def find_optimal_memory():
for memory in [128, 256, 512, 1024, 2048]:
cost = calculate_cost(memory, execution_time)
print(f"{memory}MB: " + str(cost))
# Use connection pooling
import boto3
dynamodb = boto3.resource('dynamodb')
table = dynamodb.Table('my-table')
def lambda_handler(event, context):
# Reuse connection
response = table.get_item(Key={'id': event['id']})
return response
Resources:
MyFunction:
Type: AWS::Lambda::Function
Properties:
ProvisionedConcurrencyConfig:
ProvisionedConcurrentExecutions: 10
- Right-size memory allocation
- Minimize cold starts
- Use provisioned concurrency for critical functions
- Optimize dependencies
- Use Lambda layers for common code
Optimize Lambda by right-sizing memory, reducing cold starts, and using provisioned concurrency for critical functions.
For AWS Lambda Optimization: Reducing Costs and Improving Performance, define pre-deploy checks, rollout gates, and rollback triggers before release. Track p95 latency, error rate, and cost per request for at least 24 hours after deployment. If the trend regresses from baseline, revert quickly and document the decision in the runbook.
Keep the operating model simple under pressure: one owner per change, one decision channel, and clear stop conditions. Review alert quality regularly to remove noise and ensure on-call engineers can distinguish urgent failures from routine variance.
Repeatability is the goal. Convert successful interventions into standard operating procedures and version them in the repository so future responders can execute the same flow without ambiguity.
For AWS Lambda Optimization: Reducing Costs and Improving Performance, define pre-deploy checks, rollout gates, and rollback triggers before release. Track p95 latency, error rate, and cost per request for at least 24 hours after deployment. If the trend regresses from baseline, revert quickly and document the decision in the runbook.
Keep the operating model simple under pressure: one owner per change, one decision channel, and clear stop conditions. Review alert quality regularly to remove noise and ensure on-call engineers can distinguish urgent failures from routine variance.
Repeatability is the goal. Convert successful interventions into standard operating procedures and version them in the repository so future responders can execute the same flow without ambiguity.
For AWS Lambda Optimization: Reducing Costs and Improving Performance, define pre-deploy checks, rollout gates, and rollback triggers before release. Track p95 latency, error rate, and cost per request for at least 24 hours after deployment. If the trend regresses from baseline, revert quickly and document the decision in the runbook.
Keep the operating model simple under pressure: one owner per change, one decision channel, and clear stop conditions. Review alert quality regularly to remove noise and ensure on-call engineers can distinguish urgent failures from routine variance.
Repeatability is the goal. Convert successful interventions into standard operating procedures and version them in the repository so future responders can execute the same flow without ambiguity.
For AWS Lambda Optimization: Reducing Costs and Improving Performance, define pre-deploy checks, rollout gates, and rollback triggers before release. Track p95 latency, error rate, and cost per request for at least 24 hours after deployment. If the trend regresses from baseline, revert quickly and document the decision in the runbook.
Keep the operating model simple under pressure: one owner per change, one decision channel, and clear stop conditions. Review alert quality regularly to remove noise and ensure on-call engineers can distinguish urgent failures from routine variance.
Repeatability is the goal. Convert successful interventions into standard operating procedures and version them in the repository so future responders can execute the same flow without ambiguity.
