---
title: "Request AWS Lambda Best Practices"
description: "Get serverless functions optimized for performance and cost"
category: "tips-tricks"
tags: ["aws", "lambda", "serverless", "cloud", "optimization"]
tech_stack: ["aws", "serverless", "nodejs", "python"]
---

To make your AWS Lambda functions perform better and save money, focus on some key best practices when writing your code. This way, your serverless functions will run smoothly and cost-effectively.

1. **Cold Start Optimization**: Look for ways to reduce latency during the first call to your function.
   - Try this: `Optimize for cold start by using lighter libraries and keeping the function warm.`

2. **Memory Allocation**: Find the right memory settings to match your function's workload.
   - Consider this: `Allocate memory based on performance testing results to balance speed and cost.`

3. **Efficient Packaging**: Make sure your code only includes the necessary dependencies.
   - Suggest this: `Package the function with only essential libraries to reduce deployment size.`

4. **Environment Variables**: Use strategies to manage your configuration and secrets in a secure way.
   - For example: `Use environment variables for configuration to avoid hardcoding sensitive data.`

5. **Cost Optimization Strategies**: Look for tips on how to lower your execution costs.
   - You might say: `Implement provisioned concurrency for predictable workloads to manage costs effectively.`

By following these practices, your AWS Lambda functions will run faster and be more cost-efficient.

### Why It Works
These strategies improve performance by cutting down latency and resource use, which means lower costs. Use this guidance when creating new serverless applications or fine-tuning existing functions.

### Quick Examples
- **Before**: Your functions experience slow cold starts because of heavy libraries.
  - **After**: `Optimize for cold start by using lighter libraries and keeping the function warm.`
  
- **Before**: You have hardcoded configuration values, which can be risky.
  - **After**: `Use environment variables for configuration to avoid hardcoding sensitive data.`

- **Before**: High execution costs from improper memory allocation.
  - **After**: `Allocate memory based on performance testing results to balance speed and cost.`

### Common Mistakes
- **Ignoring Cold Start Issues**: Always think about cold start optimizations for applications sensitive to latency. 
- **Over-Allocating Memory**: Experiment with different memory settings to find the best fit. 
- **Including Unnecessary Dependencies**: Review your packages to ensure only essential libraries are included.
- **Hardcoding Sensitive Information**: Always opt for environment variables to keep sensitive data secure.