Reading a recent article from AWS about the new billing policy for Lambda’s INIT phase, I couldn’t help but think about how many of us have relied on that unbilled cold start without even realizing it. It was like a free lunch we never asked for, but gladly accepted. And now, as of August 1, 2025, that quiet gift is being taken away.

At first glance, the change looks fair. AWS is simply aligning the billing model for all runtimes, whether you’re using containers, provisioned concurrency, or plain ZIP based functions. They’ve even framed it as a “standardization”, not a price hike. But let’s be honest, this is AWS we’re talking about. When they “standardize”, you usually end up paying more.

I’m not complaining. I’ve always believed pricing models should reflect real usage. But what this change really highlights is something deeper: most teams still don’t fully understand what happens during a Lambda cold start. The INIT phase was never “free”, it was just invisible. Now that it costs money, we’re all being forced to pay attention.

And that’s a good thing.

We’ve entered a new phase where optimizing the INIT time of your functions isn’t just about performance. It’s about cost. You can’t afford to pretend it doesn’t matter anymore. Whether your code is in Java, Python or Node.js, you need to understand exactly what’s happening when Lambda spins up a new environment.

So let’s break it down, not just what this change means, but how to respond with architecture that makes sense.

---

Cold Starts Speak Your Language

When AWS says the INIT phase is now billable, what they’re really saying is this: choose your runtime carefully. Cold starts have always existed, but now they come with a price tag. And depending on the language you use, that price can vary more than you think.

If you’re using Node.js, you’re in luck. Its startup is fast, lean, and ideal for short lived, event driven workloads. In most of the APIs I’ve built with Node, the cold start impact was barely noticeable. One project in particular involved real time notifications for a mobile app, we handled thousands of invocations per hour and barely saw any delay. That same efficiency now translates into lower INIT costs.

Python comes next. Slightly heavier than Node but still quick to boot, especially when your function is small and focused. I’ve used it for processing CSV files uploaded to S3, transforming the data and pushing it to DynamoDB. Cold starts didn’t really hurt us there either, mostly because the invocations weren’t latency critical. Python still offers a solid balance between speed and flexibility.

Then there’s Go. It may not be as popular in some teams, but it’s fast. Compiled to native binaries, low memory footprint, and excellent performance under pressure. Go feels like a hidden weapon when cold start latency really matters.

On the other hand, working in a Java environment, this is where the trouble begins. Java still has one of the slowest cold starts, especially when using frameworks with heavy initialization. Even with GraalVM Native Image or SnapStart, the startup time is noticeably higher compared to Node.js or Python. I’ve used both Node and Python in Lambda functions and the difference is clear. For example, a small function in Node.js handling webhook events starts in under 100 ms. In Python, I’ve used Lambdas for S3 triggered CSV processing with similar cold start performance. Java, even when optimized, feels heavier. And now, every millisecond in that startup sequence will be billed.

Still, sometimes we don’t have the luxury to switch languages. Whether because of legacy systems, team expertise or existing integrations. Java is the choice that’s already been made. So the real question becomes: how do we survive this without rewriting everything?

---

Strategies to Survive the INIT Bill

Once you accept that cold starts are no longer “free,” the next step is to ask yourself what you can actually do about it. Changing languages isn’t always an option. But changing how you architect and deploy your functions? That’s where the real savings live.

Provisioned Concurrency: Warm by Design

If your workload is latency-sensitive, provisioned concurrency is probably your best friend. It keeps execution environments initialized and ready to respond instantly. You avoid cold starts altogether. The tradeoff, of course, is cost. You pay for the idle time, even if no one’s calling your function. But for APIs, authentication endpoints, or critical workflows, that cost can be justified.

SnapStart: Snapshot and Reuse

SnapStart works by capturing a snapshot of the runtime after the INIT phase and reusing it during cold starts. It’s available for Java, .NET, and now Python. It doesn’t eliminate cold starts entirely, but it cuts down their duration significantly. You just need to make sure your INIT logic is deterministic and serializable.

Keep Your Functions Lean

The bigger your function package, the longer the cold start. Keep your code focused. Bundle only what’s needed. Use tools like esbuild, Webpack, or optimized Lambda layers. Avoid pulling in an entire framework just to use one utility.

Optimize What Runs in INIT

Every line outside the handler adds cost now. If you’re loading data, establishing connections, or running heavy logic during INIT, move it inside the handler or make it lazy. INIT is only “once per container” until scaling happens, and when it does, you pay.

Cache Strategically

Use in-memory caches, connection reuse, and avoid repeated external calls. If your function benefits from warm reuse, make it count. AWS doesn’t guarantee warm starts, but when they happen, they can save you both time and money.

---

Final Thoughts: Build with Awareness, Not Assumptions

Cold starts used to be something you accepted quietly. A side effect, an inconvenience. But now, they come with a line item on your bill. That changes things. And it should.

If you’re designing a new system from scratch, this is your chance to be intentional. Don’t just reach for your favorite stack out of habit. Choose the runtime that makes sense for the job. If you’re building a webhook handler that runs a few times an hour, Java might not be your best option. If you’re streaming thousands of events per second, maybe it is. Cost, latency, cold start frequency, they all shape what “good” looks like.

But what if the function already exists?

That’s the part no one likes to talk about. You’re not starting fresh. You’re inheriting a Lambda that someone else wrote two years ago, with heavy INIT logic and a bloated dependency tree. You can’t just rewrite it in Go. You probably can’t even touch the runtime. So the question becomes: what’s the smallest change that makes the biggest difference?

Maybe it’s shifting a few global variables inside the handler. Maybe it’s breaking a single monolithic function into two smaller ones. Or maybe it’s just adding SnapStart and calling it a day. The point is, even small improvements matter now. Because now, they have a cost.

This update from AWS isn’t just about billing. It’s a quiet reminder that architecture is always evolving. That the decisions we made when things were “free” don’t always hold up when the price shows up on the invoice.