Hey everyone! 👋 I'm still pretty new to the whole cloud infrastructure world, coming from a data analytics background. I've been working on a side project that involves a lot of small, event-driven tasks, and I initially built it all on Google Cloud Functions. I kept hearing about AWS Lambda's performance and cost, so I decided to port everything over for a direct comparison.
My setup is pretty simple: a Python function that processes a small JSON payload (usually <5KB), does some light transformation, and writes to a database. It gets triggered around 100,000 times per day. I ran the same function, with the same logic, on both platforms for a week each to compare.
Here’s what I found in terms of performance and cost:
**Performance (Average Duration):**
* **Google Cloud Functions (256MB, 1 vCPU):** ~420ms cold start, ~105ms warm execution.
* **AWS Lambda (256MB, equivalent config):** ~180ms cold start, ~72ms warm execution.
Lambda was consistently faster for my workload, especially on cold starts. The warm execution time being nearly 30% faster was a real surprise!
**Cost Breakdown (for my 100k/day volume):**
I used the pricing calculators with my actual average execution times.
* **Cloud Functions:** My bill was roughly **$18.50** for the week.
* **Lambda:** My bill came out to about **$11.20** for the week.
That's a pretty significant saving! I think the per-millisecond billing on Lambda really helped since my functions are so short-lived.
I'd love some advice from you all who've done this longer:
* Are there any hidden costs or configuration nuances I might be missing (like egress, logging)?
* For someone focused on data pipelines (thinking about triggers from Cloud Storage vs S3, Pub/Sub vs EventBridge), what are the key integration differences I should plan for?
* Any beginner-friendly monitoring setups you'd recommend to keep tracking this?