They said in the article that they were running up to 200 pods at a time. Doing some back of the envelope math, 200 pods at $300,000 year is about $0.17/hour, which is exactly what an EC2 c5.xlarge costs per hour (on demand). That has 4 vCPUs, so about 800 vCPUs during peak, with $0.0425/CPU-hour.

I do have some questions like:

* Did they estimate cost savings based on peak capacity, as though it were running 24x7x365?

* Did they use auto scaling to keep costs low?

* Were they wasting capacity by running a single-threaded app (Node-based) on multi-CPU hardware? (My guess is no, but anything is possible)

This is where the cost came from.

>The reference implementation is JavaScript, whereas our pipeline is in Go. So for years we’ve been running a fleet of jsonata-js pods on Kubernetes - Node.js processes that our Go services call over RPC. That meant that for every event (and expression) we had to serialize, send over the network, evaluate, serialize the result, and finally send it back.

But either way, we're talking $25k/mo. That's not even remotely difficult to believe.

First I thought they were AWS lambda functions, perhaps possible if they are over-provisioned for very concurrency or something similar $25k/month is in realm of possibility.

But no, the the post is talking about just RPC calls on k8s pods running docker images, for saving $300k/year, their compute bill should be well above $100M/year.

Perhaps if it was Google scale of events for billions of users daily, paired with the poorest/inefficient processing engine, using zero caching layer and very badly written rules, maybe it is possible.

Feels like it is just an SEO article designed to catch reader's attention.

It has to be satire right? Like, you aren't out of touch on this. I get engineers maybe making the argument that $300k / year on cloud is the same as 1.5 devops engineers managing in-house solutions, but for just json parsing????