Since August 2025, AWS charges you for cold starts.
Not just the latency hit. The actual INIT phase now shows up on your bill.
Benchmarks show a 22x cost increase per million invocations for functions with heavy initialization. That $0.80 line item? Now it’s $17.80.
Cold starts used to be a performance nuisance. Now they’re a budget problem.
Here is what actually works to fix them.
THE REAL COST OF A COLD START
When Lambda spins up a new execution environment, it downloads your code, initializes the runtime, and runs your init logic. That is anywhere from 100ms for Python to 5+ seconds for Java.
Netflix hit this wall with their image processing pipeline. Java’s JVM startup was adding 500-800ms of latency to every new container.
Their fix? They rewrote the service in Go. Cold starts dropped to near-zero.
You probably can’t rewrite everything in Go. But you can apply the same principle: reduce what happens before your handler runs.
THE TRICKS THAT ACTUALLY MOVE THE NEEDLE
<ol>
<li>Trim your deployment package ruthlessly. A 60MB artifact adds 250-450ms just for download and extraction. Use bundlers and Lambda Layers to strip it down.</li>
<li>Move expensive initialization to global scope, but lazy load everything else. DB connections go outside the handler. That SDK client you initialize for one edge case? Load it only when that code path fires.</li>
<li>Bump your memory allocation. Lambda scales CPU proportionally to memory. A function at 256MB gets half the CPU of one at 512MB. Faster CPU means a faster INIT phase.</li>
<li>Skip the VPC unless you truly need it. VPC-attached Lambdas carry networking overhead on cold start. Use RDS Proxy or VPC endpoints instead.</li>
</ol>
SNAPSTART: THE ONE MOST TEAMS SLEEP ON
SnapStart caches a snapshot of your initialized environment. Next cold start? Lambda resumes from the snapshot instead of running your INIT code again.
The numbers are dramatic. Java functions that took 40 seconds to cold start dropped to 594ms. That is not a typo.
It now supports Java 11+, Python 3.12+, and .NET 8+ across 23 AWS regions. And unlike Provisioned Concurrency, you don’t pay for idle capacity.
WHEN TO PAY FOR PROVISIONED CONCURRENCY
If your API serves user-facing requests with strict SLAs, Provisioned Concurrency is the only guarantee. It keeps environments warm and ready.
But for everything else? SnapStart + package optimization + smart memory allocation gets you 90% of the way there at a fraction of the cost.
Cold starts aren’t a Lambda limitation. They are an engineering decision. The teams that treat them as a first-class optimization problem save latency and money.
What’s the most effective cold start optimization you’ve rolled out?
#AWSLambda #Serverless #BackendEngineering #CloudArchitecture

Joud Awad

Since August 2025, AWS bills the Lambda INIT phase, turning cold starts from a latency nuisance into a real cost problem. Benchmarks show up to a 22x cost increase per million invocations for functions with heavy initialization. Practical mitigations include trimming deployment packages, lazy-loading non-critical SDKs, increasing memory allocation for faster CPU, and avoiding unnecessary VPC attachment. SnapStart is highlighted as an underused feature that caches initialized environments, reducing Java cold starts from 40 seconds to ~594ms, now supporting Java 11+, Python 3.12+, and .NET 8+. Provisioned Concurrency remains the right choice only for strict SLA user-facing APIs, while SnapStart plus optimization covers most other cases at far lower cost.

Lambda Cold Start