harpratap

Dental and vanity surgeries aren't happening in a vacuum. There are baseline costs eg. anesthesia, recovery medications, medical machinery etc which are all bloated due to the rest of industry not being under price pressure (rising tide lift all boats)

It's similar to how AI data center buildout race is raising the prices for consumer electronics in 2026 and beyond. The suppliers have no incentive to sell lower cost products to tiny niche

Because insurance companies incentivize upward price momentum. The ones who innovate and bring the prices down are not rewarded for their efforts. Health inflation is higher than headline inflation because of this absence of price pressure

This is very good use case of micro-transactions. If AWS makes $100 off Redis, they should be pay back X% to Redis project, from which the money is distributed to contributors based on how important their contributions were. Also Redis project is also supposed to pay back to the software components and 3rd party libraries it uses, so C project gets a fair share of the pie contributed back to them as well.

Is that really a problem in Cloud environments where you would typically use a Cluster Autoscaler? GKE has "optimize-utilization" profile or you could use a descheduler to binpack your nodes better

Wouldn't the network cost be absurd in such case? Not only the pod-to-pod communication cost skyrocket, all the heartbeats, health checks, metrics, daemonsets pinging each other will probably end up costing more than the CPU and Memory

GKE Autopilot is pretty much useless, very few cases where it actually turns out cheaper than simply using Cluster Autoscaler + Node autoprovisioning. Not only is the pricing absolutely absurd, they don't even allow normal K8s bursting behavior (requests need to be equal to limits) which means you not only end up paying more than regular K8s cluster but now also need to highly overprovision your pods

Datadog allows you to export all of this but I don't see how that's any useful. You can't really port Datadog dashboard to let's say Grafana easily. The query languages they use do not have 1:1 mapping, the way dashboards are organized and the different visualization tools you get are not same either

> unless your a whale they give zero fucks about giving you any flexibility on price.

Even the flexible pricing they offer ends up being a sham. It just seems better on paper but you end up paying nearly the same because they have a really complicated billing model where they give you free stuff with Infra hosts, once you switch away from this model you stop getting those freebies, so your Infra hosts might cost less now but everything else is more expensive now. The house always wins!

How do you know the feeling(s) you are optimizing for are the right ones? A murderer is also optimizing for a feeling.

This is not a bare-metal vs Cloud comparison, the Cuber PaaS is supposed to run on anything including cloud based on their docs. So when they say "save 80% on cloud costs" I am assuming they mean for eg. save 80% on using Cuber on GCP compared to deploying same workload on GKE.

It would be very absurd to claim something like - I was using i9-13900KS but realized I could run the same workload on my raspberry pi, but hey I also used this packaging tool in the process therefore I saved 80% costs because of the packaging tool.

> We had 100% uptime and we saved 80% on cloud costs.

Is there a source on such bold claims?

GKE in general seems to be doing better than EKS though. They have a lot of things right and EKS seems to be playing catch-up. For example if you look at any Multi-cluster Kubernetes setup by AWS it's just a giant duct-tape rather than a ground-up solution. GKE worked on fundamentals first like multi-cluster endpoints, multi-cluster services, multi-cluster ingress, multi-cluster config-sync and now bringing it all together under GKE Enterprise.

It takes very long time for going from preview to actual production usage for anyone. We had T2D preview access more than a year ago, it took several months to get enough stock in Tokyo region (US always gets preference in such cases whenever a new machine type comes out). GCP already has Zen4 in preview for some US customers. Also, us being one of the largest GCP customers in Japan made things even slower