How to Reduce AWS Lambda Costs Without Hurting Performance

Many teams move to AWS Lambda expecting cost savings, only to be surprised when their monthly bill skyrockets. While Lambda’s pay-per-use pricing seems efficient, costs can spiral out of control if functions aren’t properly optimized. Cutting costs isn’t just about minimizing memory—sometimes, allocating more memory speeds up execution and reduces overall expenses. And in some cases, Lambda isn’t the best fit at all.

Understanding when to scale up, when to consolidate functions, and when to move workloads to ECS or EC2 is key to keeping cloud costs under control. Let’s break it down.

Right-Sizing Your Lambda Functions for Cost Savings

AWS charges for Lambda based on memory allocation and execution time. But what many overlook is that CPU power scales with memory. A function with minimal memory may take twice as long to execute compared to one with more memory—driving up the total cost.

For example, a function processing large files in S3 might take 10 seconds with 256MB of memory but only 3 seconds with 1GB. Despite the higher memory allocation, the function completes much faster, meaning you pay for fewer total milliseconds of execution time.

The key is to benchmark different memory allocations and find the point where performance gains offset the increased per-millisecond cost. AWS Lambda Power Tuning tools can help automate this process.

AWS Lambda can cost a lot

Reuse Database Connections to Avoid Unnecessary Costs

A common mistake in Lambda functions that interact with databases is opening a new connection on every invocation. Since Lambda initializes a new execution environment for each function call, placing the database connection inside the handler function causes a fresh connection to be created every time—leading to higher latency and unnecessary database costs.

Instead, initialize the connection outside the function handler. This allows Lambda to reuse the same connection across multiple executions within the same environment, reducing overhead and improving performance.

import pymysql

# Establish connection outside the handler
db_connection = pymysql.connect(host="database.example.com", user="user", password="password", database="dbname")

def lambda_handler(event, context):
    with db_connection.cursor() as cursor:
        cursor.execute("SELECT * FROM my_table")
        result = cursor.fetchall()
    return result

By keeping the connection alive between invocations, you reduce connection churn and speed up database queries, leading to lower overall costs.

The Hidden Costs of Lambda: Logs, API Gateway, and Data Transfer

Execution time isn’t the only thing you pay for. CloudWatch logs, API Gateway requests, and data transfer costs can sometimes exceed the actual Lambda compute cost.

CloudWatch logging: Writing excessive logs can be expensive, especially at high invocation rates. Consider reducing log verbosity or sampling logs to keep ingestion costs down.
API Gateway pricing: Using Lambda behind API Gateway? At high scale, switching to ALB (Application Load Balancer) could reduce request costs significantly.
Data transfer fees: Moving large amounts of data between services (especially cross-region) can lead to unexpected charges. Where possible, keep workloads within the same region and VPC.

When Lambda Isn’t the Right Tool for the Job

Lambda is great for bursty workloads, but for high-volume, steady-state processing, ECS with spot instances can be dramatically cheaper. Some companies are spending hundreds of thousands per month on Lambda, when running the same workload on ECS would cost a fraction of that.

If your workload:

Has consistent traffic patterns (e.g., processing jobs every few minutes)
Requires high memory or CPU for extended periods
Can batch process requests instead of running per event

Then ECS on spot instances or Fargate with auto-scaling might be a better fit. This allows you to fully utilize allocated resources rather than being charged per millisecond.

Lambda Function Consolidation: Fewer, Bigger Functions Can Cost Less

A common mistake is breaking applications into too many small Lambda functions. Each function runs in its own environment, meaning you’re paying for cold starts and context switching.

If your functions:

Have shared dependencies
Frequently invoke each other
Spend most of their time waiting on API calls

Consider combining them into a single function with parallel execution. Instead of running three separate functions that each execute for 10 seconds, a single function running all three in parallel could complete in the time of the longest-running task—cutting total billed time by a third.

Final Thoughts

Reducing Lambda costs isn’t just about cutting memory or limiting invocations. The real savings come from right-sizing function memory, optimizing execution time, and knowing when to move workloads to ECS. By benchmarking performance, reusing database connections, reducing hidden costs, and consolidating functions, teams can dramatically lower their AWS bill without sacrificing performance.