Serverless sounds simple. No servers to manage. Auto-scaling out of the box. Billing by the millisecond. What’s not to love?
But under that sleek surface is a messier truth—one that usually shows up after you’ve gone all-in. And often, it shows up as a much bigger bill than you expected.
Welcome to AWS Lambda sticker shock.
The Allure (and the Trap) of "Simple"
Meet Alex. DevOps engineer. Assigned to migrate a clunky old monolith to serverless.
The docs make it sound like a no-brainer. Lambda is cheaper, faster, easier. So Alex breaks the app into microservices, deploys to AWS, and waits for the cost savings to show up.
They don’t.
Instead, the monthly bill triples.
Where the Bill Blows Up
Real story: a team moved a small e-commerce backend to Lambda. About 30,000 requests a day. Each request kicked off up to 15 separate functions. Average runtime? Around 100ms.
Seemed efficient on paper.
But they ended up burning $1,500 more per month. Why? Because Lambda doesn’t just charge for time. It charges per invocation. And when you multiply 15 functions by 30,000 requests, every day, it adds up fast.
That’s the part nobody tells you: more functions = more money.
Cold Starts: The Latency You Didn’t Plan For
Lambda has a hidden tax. It’s called a cold start. Basically, if your function hasn’t run in a while, AWS takes a moment to spin it up. That delay can be hundreds of milliseconds.
One team building a chat interface saw cold starts averaging 400ms. That’s 20% of their total response time—just waiting for the function to wake up.
Worse, AWS still bills you for it. Startup lag and all.
Automation Isn’t Optimization
Let’s say you deploy Lambda using Terraform. Something like this:
resource "aws_lambda_function" "my_function" {
function_name = "my_function"
s3_bucket = "my_bucket"
s3_key = "my_function.zip"
handler = "index.handler"
runtime = "nodejs14.x"
memory_size = 128
timeout = 3
environment = {
TABLE_NAME = aws_dynamodb_table.my_table.name
}
role = aws_iam_role.lambda_exec.arn
}
Awesome. It’s automated. But that doesn’t mean it’s optimized. The real savings happen inside the function.
Cut Waste Early
In one project, a client was spending close to $2,000/month on Lambda. The issue? A function that often returned empty results. Bad data was slipping through, getting processed anyway, and racking up charges.
Here’s the simple fix that saved them $800/month:
exports.handler = async (event) => {
if (!event.data || !event.data.length) {
console.warn("No data to process!");
return "No data processed";
}
// Process event.data here
};
One small check. Big impact. When these kinds of micro-fixes add up, your bill starts to look very different.
How to Actually Save Money with Lambda
Forget "set it and forget it." Cost-efficient Lambda takes planning. Here’s where to start:
- Limit function chaining: Too many small functions? Combine them.
- Right-size memory: More memory = more speed and more cost. Tune it.
- Keep hot paths warm: Use provisioned concurrency or schedule pings.
- Shrink your packages: Less code = faster cold starts.
- Watch everything: CloudWatch and Cost Explorer are your best friends.
Bottom Line: Pay Attention or Pay Up
Lambda feels like magic. No servers, auto-scaling, per-millisecond billing. But the magic has limits. Costs sneak in through small inefficiencies, and before you know it, you're way over budget.
Milliseconds matter. Invocations matter. Cold starts matter.
So audit often. Design carefully. And always, always read your AWS bill.
Because in serverless, what you don’t see will cost you.