7 AWS Lambda Secrets That Slashed Your Bill by 70%

AWS Lambda and the term serverless are sometimes labelled as an “expensive” alternative to server-based development.

But here’s the little-known secret: it can be shockingly affordable when you know what you’re doing.

Here’s exactly how one company cut its Lambda bill from $8,200 to $2,460 per month in just 24 hours by uncovering what most teams miss.

1. Boosting Memory to Cut Costs

More memory = higher cost, right? Not always.

Because AWS allocates CPU power based on memory size, upping memory can drastically reduce execution time. In the company’s tests, increasing memory lowered the total cost for many functions.

• Sweet spot: 512MB for most I/O-heavy functions

• Tip: Benchmark your functions at multiple memory settings — every function is different.

2. Cold Starts Were Eating 25% of Their Budget

Provisioned Concurrency isn’t always the answer.

Instead of blindly applying it, they used CloudWatch Logs Insights to measure cold start frequency and impact, then they selectively enabled Provisioned Concurrency only where it saved money.

Use lazy initialization for dependencies (only load what’s needed, when it’s needed)

👉 Result: 40–60% reduction in cold start time

3. The Polling Pattern That Cost them Thousands

One function was checking an SQS queue every second, and wasting over 2.6M invocations per month.

They switched to event-driven architecture:

• SQS → triggers Lambda directly

• Or used long polling where needed (up to 20 seconds)

👉 Result: 95% reduction in invocations, saving $500/month

4. Trimming dependencies to remove dead weight

The company trimmed its dependency trees and used Lambda Layers for shared packages like pandas, axios, or aws-sdk.

Bonus:

• Cold starts dropped significantly

• Deployment sizes shrank by up to 95%

• They even built a custom Node.js runtime for precompiled dependencies, saving 800ms per cold start

5. Optimizing the Lambda Lifecycle

They found major gains by:

• Reusing database and API connections between invocations

• Tuning timeouts (no more 3-second hangs for 300ms jobs)

• Using async batch processing to parallelize workloads

👉 Result: These changes slashed execution times by up to 70% in high-volume paths.

6. Batching Requests

Instead of triggering Lambda for every SQS message, they batched them (up to 10 per invocation).

For high-throughput systems, that meant:

• 5M daily messages → 500K Lambda runs instead of 5M

• 70% reduction in compute hours

• Huge cost drop with no performance loss

For services without native batching, they added a custom EventBridge → SQS aggregator. Simple but super effective.

7. Architecting for Cost, Not Just Features

They broke up monolithic Lambdas into Step Functions, this allowed them to:

• Right-size each function’s memory

• Apply timeouts/retries at the step level

• Slash Lambda execution costs by up to 40% in complex workflows

For long-running, CPU-heavy jobs, they triggered Fargate containers from Lambda.

Same event-driven architecture but at a much lower cost.

Want to learn more about optimizing your cloud costs? Subscribe to our newsletter for weekly cloud computing cost-savings tips and insights.

Curated Article

1. “The Hidden AWS Lambda Secrets That Cut My Bill by 70% Overnight”. Coders Stop. April 10, 2025. https://aws.plainenglish.io/the-hidden-aws-lambda-secrets-that-cut-my-bill-by-70-overnight-ca7a8e9db7dc

Enjoying the newsletter?

🚀 Follow me on LinkedIn for daily posts on AWS and saving money with the cloud.

🔥 Invite your colleagues to subscribe to help them save on their AWS costs.

✍️ Check out my blog on AWS here.