A pattern keeps repeating itself in AI projects.
The model works.
The demo works.
The proof of concept gets approved.
Then someone asks the que...
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Great writing.
One nuance worth adding to the AI app case: the PaaS vs K8s choice changes when your "app" includes an agent that wants to run code, spin up services, or write to a database. Then you need a PaaS that doesn't hide the container layer too aggressively, otherwise the agent loses the affordances it needs.
We tried solving exactly that with ZCP at Zerops (where I work), where the PaaS ergonomics stay but the agent gets a real Linux box to operate in. Liked the post.
Thank you.
That's an interesting nuance, and I think it's one that's becoming increasingly relevant as agent-based applications evolve beyond simple API orchestration.
A lot of the deployment discussions today assume the application is mostly serving requests. But once agents start executing code, provisioning resources, interacting directly with databases, or managing services as part of their workflow, the infrastructure requirements change quite a bit. At that point, it's not just about hosting an application... it's about providing an environment where the agent can safely and effectively operate.
Also, you're right about PaaS platforms potentially abstracting away too much of the underlying environment. Simplicity is valuable, but there are definitely cases where developers (or agents) need access to lower-level capabilities without taking on the full operational burden of Kubernetes.
It's an interesting middle ground that I didn't explore in the article, and I suspect we'll see more platforms moving in that direction as agentic systems become more common.
Thanks for sharing what you're building at Zerops.
Really enjoyed reading it.
Appreciate how you don’t try to villainize Kubernetes; you just put it in the right place on the spectrum. A lot of infra discussions miss this.
This feels like the kind of article I wish more teams read before over-engineering their first deployment.
Thanks for sharing.
That means a lot. Thank you.
You're right! Most teams don’t fail because they picked the “wrong” tool; they fail because they adopt complexity before they actually need it. And once you’re deep in that complexity, it’s hard to step back.
Really glad it resonated with you.
Hadil, this was a good reminder that not every project needs Kubernetes from day one. Sometimes the simplest solution really is the right one, at least until the project gives you a reason to add more complexity.
Thanks! That's exactly the point I was trying to make. There's nothing wrong with Kubernetes, but complexity should come from real requirements, not assumptions about future scale.
A lot of teams can go far with much simpler setups before orchestration becomes necessary.
Thanks for sharing!
Welcome! 🙌🏻
Great article! Excellent content. Nice visuals too. A++
Thank you so much, James 😍 Glad you found it helpful.
The article highlights that Kubernetes is often overkill for early AI apps due to high complexity. It's better to start with Docker or PaaS, adopting K8s only for multi-model, large-scale systems.
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