Cut Your AWS Costs by 80% With This One Tip

Many developers assume scaling up infrastructure is the only way to handle performance issues in their Node.js applications.

But what if the problem isn’t your AWS resources but your code?

That was exactly the situation one developer faced. Instead of adding more EC2 instances and increasing Lambda memory, they optimized their Node.js code—and the result was an 80% reduction in AWS costs.

Here’s how they did it.

Their Node.js app ran on AWS with EC2, Lambda, DynamoDB, and S3.

Despite auto-scaling, they noticed:

• High CPU and memory usage: EC2 instances maxed out faster than expected.

• Long Lambda execution times: simple functions were running longer than necessary.

• Costly DynamoDB read/write operations: Unoptimized queries were driving up expenses.

The solution?

Optimizing the application instead of the infrastructure.

1. Finding Bottlenecks with Profiling

Using tools like Node.js Profiler, AWS X-Ray, and clinic.js, they identified areas where performance suffered:

• Blocking synchronous operations causing delays.

• Redundant database queries fetching unnecessary data.

• Heavy JSON parsing slowing down response times.

2. Optimizing Asynchronous Operations

A common mistake in Node.js apps is running async operations sequentially instead of in parallel. By switching to Promise.all(), they significantly reduced wait times, cutting Lambda execution costs.

Before:

const userData = await fetchUserData(userId);

const orders = await fetchUserOrders(userId);

const recommendations = await fetchRecommendations(userId) ;

After:

const [userData, orders, recommendations] = await Promise.all([

fetchUserData(userId),

fetchUserOrders(userId),

fetchRecommendations(userId),

]);

3. Caching to Reduce Database Costs

DynamoDB queries were draining resources.

The fix?

Implementing Redis caching for frequently accessed data like user profiles, reducing read requests by 40%.

4. Reducing Payload Sizes

API responses were sending excessive data. They solved this by:

• Implementing GraphQL to fetch only the necessary fields.

• Compressing files before storing them in S3, slashing storage costs.

5. Leveraging AWS Services Efficiently

Lambda: Upgraded to the latest Node.js runtime for faster cold starts.

DynamoDB: Moved from provisioned capacity to on-demand to match usage.

EC2 Autoscaling: Adjusted policies to prevent over-provisioning.

The Results: Massive Savings

After these optimizations, their AWS bill transformed:

• 50% drop in EC2 costs—Fewer instances needed.

• 60% reduction in Lambda costs—Faster execution times.

• 40% lower DynamoDB costs—Caching and smarter queries.

• Overall 80% AWS cost reduction.

Takeaways for Your Own AWS Setup

• Fix your code before scaling. Inefficiencies waste resources, driving up costs.

• Use profiling tools. You can’t optimize what you don’t measure.

• Understand AWS services. DynamoDB, Lambda, and EC2 can be cost-efficient—or expensive—depending on how you use them

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Curated Article

1. “My AWS bills decreased by 80% simply by optimizing my Node.js code.”. Cloud Guru. Jan 19, 2025. https://towardsaws.com/my-aws-bills-decreased-by-80-simply-by-optimizing-my-node-js-code-a67f94013d41

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