I started with kubernetes and have never looked back. Being able to bring up a network copy, deploy a clustered database, deploy a distributed fs all in 10 minutes (including the install of k3s or k8s) has been a game-changer for me.
You can run monolithic apps with no downtime restarts quite easily with k8s using rollout restart policy which is very useful when applications take minutes to start.
Every time I see one of these posts and the ensuing comments I always get a little bit of inverse imposter syndrome. All of these people saying "Unless you're at 10k users+ scale you don't need k8s". If you're running a personal project with a single-digit user count, then sure, but only purely out of a cost-to-performance metric would I say k8s is unreasonable. Any scale larger, however, and I struggle to reconcile this position with the reality that anything with a consistent user base should have zero-downtime deployments, load balancing, etc. Maybe I'm just incredibly OOTL, but when did these simple features to implement and essentially free from a cost standpoint become optional? Perhaps I'm just misunderstanding the argument, and the argument is that you should use a Fly or Vercel-esque platform that provides some of these benefits without needing to configure k8s. Still, the problem with this mindset is that vendor lock-in is a lot harder to correct once a platform is in production and being used consistently without prolonged downtime.
Personally, I would do early builds with Fly and once I saw a consistent userbase I'd switch to k8s for scale, but this is purely due to the cost of a minimal k8s instance (especially on GKE or EKS). This, in essence, allows scaling from ~0 to ~1M+ with the only bottleneck being DB scaling (if you're using a single DB like CloudSQL).
Still, I wish I could reconcile my personal disconnect with the majority of people here who regard k8s as overly complicated and unnecessary. Are there really that many shops out there who consider the advantages of k8s above them or are they just achieving the same result in a different manner?
One could certainly learn enough k8s in a weekend to deploy a simple cluster. Now I'm not recommending this for someone's company's production instance, due to the foot guns if improperly configured, but the argument of k8s being too complicated to learn seems unfounded.
I've been in your shoes for quite a long time. By now I've accepted that a lot of folks on HN and other similar forums simply don't know / care about the issue that Kubernetes resolves, or that someone else in their company takes care of those for them
k8s makes it easier to build over engineered architectures for applications that don’t need that level of complexity
So while you are correct that it is not actually that difficult to learn and implement K8S it’s also almost always completely unnecessary even at the largest scale
given that you can do the largest scale stuff without it and you should do most small scale stuff without it, the number of people for whom all of the risks and costs balancr out is much smaller than the amount that it has been promoted and pushed
And given the fact that orchestration layers are a critical part of infrastructure, handing over or changing the data environment relationship in a multilayer computing environment to such an extent is a non-trivial one-way door
I can bring up a service, connect it to a postgres/redis/minio instance, and do almost anything locally that I can do in the cloud. It's a massive help for iterating.
There is a learning curve, but you learn it and you can do so damn much so damn easily.
+1 on the learning curve, took me 3 attempts (gave up twice) before I spent 1 day learning the docs, then wasting a week moving some of my personal things to it.
Now I have a small personal cluster with machines and vps's (on some regions I don't have enough deployments to justify an entire machine) with a distributed multi-site fs that's mostly as certified for workloads as any other cloud. CDN, GeoDNS, nameservers all handled within the cluster. Any machine can go offline while connectivity remains the same, minus the timeout requirement of 5 minutes of downed pods to be rescheduled for monolithic services.
Kubernetes also provides an amazing way to learn things like bgp, ipam and many other things via calico, metallb and whatever else you want to learn.
You can run monolithic apps with no downtime restarts quite easily with k8s using rollout restart policy which is very useful when applications take minutes to start.