November 21, 2022—It’s been an eventful month in tech. The AI/LLM infrastructure landscape is exploding post-ChatGPT, platform engineering has become mainstream, and FinOps, cloud cost pressure, and DORA metrics are all the rage. I found myself wrestling with one of those pesky serverless functions that seemed to run away with our budget like a wild horse.

The Setup

We had recently upgraded our app to use AWS Lambda, aiming for the benefits of serverless—faster deployments, easier scaling, and cost savings. But as usual, the devil was in the details. What started out as a simple function quickly grew into a beast that demanded attention.

The Problem

One day, I logged into the AWS Cost Explorer to see if everything was under control. My eyes widened as I saw the numbers—our Lambda costs had doubled over the last few weeks. I decided to dive deeper and track down the culprit.

I opened up the CloudWatch logs for a specific function that seemed to be the most active one. The logs were filled with messages, but they didn’t immediately tell me what was going wrong. It was like trying to find a needle in a haystack, except both the needle and hay were moving.

Debugging

After some digging, I realized that the issue lay not just within the function itself, but also in its interactions with other services. The function was triggered by S3 events, which meant it would execute whenever new files hit the bucket. But somehow, these executions had spiraled out of control.

I spent hours analyzing the logs and adjusting the permissions and triggers to ensure that only the necessary files were being processed. I added rate limits to the function to prevent it from firing too frequently, but this just sent me down another rabbit hole. The function was still running like a hamster on a wheel, eating up resources.

The Solution

In the end, the solution came from a combination of code changes and AWS Cost Management tools. I went through the function’s code line by line, ensuring that each operation was as efficient as possible. I also set up detailed cost alerts to monitor the spending in real-time. This allowed me to catch any unexpected spikes early on.

But the real breakthrough came when I used AWS Budgets and AWS Trusted Advisor to gain a broader view of our cloud usage. These tools helped me identify other services that were contributing to our high costs, not just Lambda. For example, I found that our S3 bucket was storing unnecessary data, which led to increased storage fees.

Lessons Learned

This experience taught me the importance of monitoring and managing serverless functions proactively. It’s easy to get caught up in the excitement of deploying new features without thinking about the long-term cost implications. As platform engineers, we need to be mindful of not just the code we write but also how it integrates with the rest of our infrastructure.

The Future

Looking ahead, I’m excited to see how these lessons will shape my approach to serverless architecture in the future. We’ll continue to leverage tools like AWS Budgets and Trusted Advisor to stay on top of costs, while also focusing on optimizing our functions for both performance and efficiency.

As we move forward into an era where FinOps is becoming more mainstream, I’m confident that staying vigilant will help us keep those serverless budgets in check. After all, a well-managed cloud environment isn’t just about cost savings—it’s about making the most of the resources available to us.

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This was just one chapter in our ongoing saga with AWS and serverless functions, but it was an important lesson for sure. Debugging the beast can be tricky, but staying persistent and using the right tools can lead to significant improvements.