Title: Debugging the Serverless Mirage

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Today’s blog post is a bit of a reflection on some real ops and infrastructure work I’ve been tackling recently. It’s also a nod to the current tech climate where serverless is everywhere but harder than ever to make it truly seamless.

The Serverless Mirage

It started with an internal platform issue that was causing a lot of head scratching. Our users were reporting latency spikes, and the metrics didn’t seem to align with what we expected from our serverless architecture. We had a robust monitoring stack—Prometheus, Grafana, Jaeger for tracing—but something wasn’t adding up.

After hours of digging through logs and traces, I realized that the problem lay in how Lambda functions were being invoked. Our application was using AWS EventBridge to trigger these lambda functions via scheduled events. The issue was, EventBridge had a delay—a minimum invocation latency—which we hadn’t accounted for properly.

The irony here is that serverless is supposed to abstract away much of this complexity, but sometimes the devil’s in the details. I ended up writing a small script to simulate the behavior of our application under different conditions and slowly ramped up the number of events being sent through EventBridge. It wasn’t until I added a significant amount of load that the latency spike became noticeable.

The Cost Conundrum

While debugging, I couldn’t help but reflect on the broader cost pressures in the tech landscape. With FinOps becoming more mainstream, every penny counts. Our team has been under pressure to optimize our spending while still delivering value. This led me to explore Serverless Framework’s built-in feature for optimizing cold starts—something that can significantly reduce costs.

We started by tweaking our function configurations and redeploying them. The savings were modest but noticeable. But then, the real challenge came when I dug into the AWS Cost Explorer data. It turns out, some of these optimizations were only moving the cost around rather than reducing it overall.

This got me thinking about DORA metrics again—deploy frequency, lead time for changes, systemINavigation time, and mean time to recovery. We’ve been tracking them closely, but as I sat there analyzing our billing data, it felt like we needed a more holistic view of costs that went beyond just the cloud provider’s bill.

The WebAssembly Revolution

While not directly related to this serverless issue, the rise of WebAssembly (Wasm) on the server side has been fascinating. There’s a growing trend where developers are exploring Wasm for backend tasks—especially in microservices and serverless architectures. It promises faster execution times and potentially more efficient resource utilization.

However, I’ve found that while the technology is promising, it’s still not quite ready for prime time in production environments. The tools and frameworks around WebAssembly are evolving rapidly but can be brittle. We’ve started experimenting with a few Wasm-based services on the side, but so far, we’re sticking to well-established serverless offerings like AWS Lambda.

Reflections

Reflecting on this experience, I realize that while technology moves at breakneck speed, the core challenges of ops and infrastructure engineering remain. Whether it’s debugging latency issues in a serverless architecture or optimizing costs with FinOps, these problems are often rooted in the same fundamental principles—performance, reliability, and cost.

As we navigate through the chaos of AI/LLMs and platform engineering, it’s important to ground ourselves in practical solutions. Debugging real-world issues can be frustrating but also incredibly rewarding. It helps us understand not just how things should work on paper, but how they really behave in production.

So here’s to more late nights with logs and traces, and the constant evolution of tech that keeps us all on our toes!

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Feel free to reach out if you’ve had any interesting ops or infrastructure challenges to share!