Lambda pricing is calculated as a combination of:
1. Total number of requests
2. Total duration of invocations
3. Configured memory allocated
We can follow the best practices' to minimize cost
- Rightsiz the functions: Analyze the memory and CPU requirements of your Lambda functions, start with lowest as possible in lower environments and adjust as per needs when you are going live. Adjust the allocated memory to match your actual needs to avoid overprovisioning. Assume your Lambda function executes with 512Mb memory at worst case too, there is no gain in assigning 1024Mb for the same, assign the memory optimistically.
Monitor the performance of your functions and fine-tune resource allocation accordingly, AWS Cost optimizer can be helpful.
- Use Provisioned Concurrency: Use provisioned concurrency to keep a certain number of warm instances ready.
This reduces cold start times and can help manage costs for functions with frequent cold starts.
- Optimize Function Code: Review your code for inefficiencies and optimize it for execution time. Minimize unnecessary dependencies and imports, reduce the bundle size. (reducing bundle size will reduce some memory consumption like java meta space(PermGen). Use function initialization to perform setup tasks outside the handler.
- Enable Function Timeout: Set an appropriate timeout for your functions to avoid unnecessary execution costs, where if you are interacting with low performing 3rd party APIs or some un responsive downstream systems. Functions should terminate as soon as they complete their tasks.
- Use Environment Variables: Store configuration settings and sensitive data as environment variables. This allows you to update settings without modifying code, reducing deployment overhead.
- Implement Caching: Cache frequently used data or results to reduce redundant calculations or API calls. Use services like Amazon Elasticache for caching.
- Optimize Trigger Configurations: Review the trigger configurations (e.g., event sources like S3, SQS, API Gateway) for your Lambda functions. Subscribe to what is really needed than all events on that service. Ensure that event payloads are efficiently processed.
- Leverage Batch Processing: If possible, batch process data to minimize the number of function invocations. Where as invocations are costly than longer executing lambda. Assume 5x100ms invocations to process 5 events over 1 invocation takes <500ms. This can reduce the overhead of individual function executions.
- Proper Error Handling: Implement robust error handling to prevent unnecessary retries and failed executions. Configure dead-letter queues (DLQs) for failed events to analyze and address issues.
- Monitor and Analyze: Use AWS CloudWatch to monitor Lambda function performance, execution counts, and duration. Set up alerts to notify you of anomalies or unexpected usage. Analyze CloudWatch metrics to identify optimization opportunities.
- Use AWS Lambda Layers: Store common code, libraries, and dependencies in Lambda Layers to reduce the deployment package size. This can help reduce cold start times and deployment duration.
- Cleanup Unused Resources: Periodically review and remove unused Lambda functions, layers, and associated resources. Unused resources can accumulate costs over time.
- Leverage Reserved Concurrency: For steady-state workloads, consider using reserved concurrency to limit the number of concurrent executions and manage costs.
