You probably shouldn’t be using Kubernetes for your new startup
Tags: devops, kubernetes, startup
Published: 2019-11-03 21:20:25

---
title: You probably shouldn’t be using Kubernetes for your new startup
published: true
date: 2019-11-03 21:08:00 UTC
tags: devops, kubernetes, startups
canonical_url: https://chrisdodds.net/you-probably-shouldnt-be-using-kubernetes-for-your-new-startup/
cover_image: https://chrisdodds.net/wp-content/uploads/2019/11/frank-eiffert-G9gHtroxnaI-unsplash-2000x1200.jpg
---

Kubernetes ([k8s](https://github.com/kubernetes/kubernetes)) is awesome. I am a fan. I like it so much I have a k8s-themed license plate on my car, but I cringe every time I see a blog post or tweet pitching it as a solution for a nascent company.

Like microservices (Which, strong opinion incoming… you also probably shouldn’t be using, especially for something new. Seriously, stop. ), k8s solves a specific set of problems (mostly coordination/abstraction of some infrastructure components/ deployments and a lot of stuff related to general scaling/self-healing) and comes with significant, usually overlooked cost.

From a sysadmin perspective, k8s is borderline magic. Compared to all the bespoke automation one might have had to build in the past, k8s provides a general purpose infrastructure-as-code path that \*just works\*. A metaphor of Lego-like bricks that snap together is apt… for the most part.

K8s abstracts away a huge amount of complexity that normally consumes the lives of sysadmins , but the complexity is still there. It’s just gracefully hidden 95% of the time and the way it bubbles up is binary. Problems in k8s are either incredibly easy to solve or incredibly difficult. There’s not much in-between. You’re either building with Lego or troubleshooting polymers at the molecular level.

Deploying a completely new service, configured from ingress to database in an easy-to-read YAML file? – Super simple.

Understanding the interplay of infra-service network optimizations and failure modes? – Even with tools like [service meshes](https://linkerd.io/) and advanced monitoring/introspection, it’s really difficult.

Cluster security, networking controls, third-party plugins? Now you’re in deep, specific-domain-knowledge land.

Hosted-k8s (EKS, AKS, GKE, etc.) does not solve these problems for you. \*\*Caveat: I know there are some **fully-managed** k8s providers popping up, but the best of those are basically Platform-as-a-Service (PaaS). \*\* It solves a lot of other problems related to the care and feeding of the k8s control plane, but you’re still left with complexity that’s inherent to running services within a cluster. Even if you’re a ninth-level Linux witch, there are problems that arise when running clustered infrastructure at scale that are simply \*hard\* in a similar (but admittedly less-complex) way that neuroscience is hard.

There is a point at which the challenge of this hidden complexity begins to be outweighed by the benefits of k8s, but it’s pretty far down the road – we’re talking many-millions-of-requests-per-day-with-several-tiers/services-and-possibly-geographies territory. Or you’re in a very specific niche that requires complex auto-scaling machine learning fleets, or something similar.

This is not intended as fear mongering. Again, I use k8s everyday and think it is awesome, but you need to go into it with eyes wide open and only after you’ve leaned hard into the constraints of PaaS or more traditional, boring tech that you fully grok. I started using k8s with this perspective, (at least I think I did) and there were still surprises along the way. It’s not a panacea. It’s highly unlikely that using k8s is going to save your company. Like most technologies, it will cause as many problems as it solves, you just need to have a solid understanding and rationale around which set of problems you want and are capable of dealing with.

If you’re building a new company or product, troubleshooting k8s is likely not in one of the problem sets you should be dealing with. Use Heroku or Elastic Beanstalk or whatever else that takes care of the undifferentiated heavy lifting for you. You can circle back to k8s when things really start cooking and you’ve got the people and resources to keep things on track.

None of this is to say you shouldn’t learn k8s or play around with [minikube](https://github.com/kubernetes/minikube) in development. Just keep in mind the huge difference between mastering k8s on your local machine and operationalizing it in production.

You could replace “k8s” with pretty much any technology and I think this advice would still apply. If you’re building something new, focus on the things that really move the needle and don’t try to solve architectural problems that you don’t have.

Photo by [Frank Eiffert](https://unsplash.com/@feiffert?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText) on [Unsplash](https://unsplash.com/s/photos/helm?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText)