Platform Guide

CaaS vs PaaS: Who Owns What

Every -aaS acronym is the same question wearing a different hat: which layers of the stack do you control, and which do you hand to a provider? Here's the ladder, rung by rung — and how to pick without regretting it at scale.

3

Layers of abstraction to choose between

66%

Of orgs run AI workloads on Kubernetes (CNCF)

5

Managed K8s services in our practice: EKS · AKS · GKE · OKE · DOKS

0

Lock-in when the orchestration layer is open source

Overview

The -aaS Ladder, in Plain Terms

IaaS, CaaS, and PaaS aren't competing products — they're rungs on one ladder of delegation. Each step up hands another slice of the stack to a provider: first the hardware, then the orchestration control plane, then the entire runtime. Each step down returns control, along with the operational work that comes with it.

The vendor labels muddy this constantly, so anchor on one question: when something needs patching, scaling, or debugging at 2 a.m., whose pager goes off? Answer that for the OS, the orchestrator, and the application, and you've classified any service on the market — whatever its marketing calls it.

Definitions

Three Layers, Three Deals

Same stack, different dividing lines. What each model actually hands you — and what it quietly keeps.

IaaS

Infrastructure as a Service

Raw compute — you run everything

The provider hands you virtual machines, block storage, and a network. Everything above the hypervisor is yours: operating systems, patching, runtimes, orchestration, scaling logic, and every 2 a.m. page that comes with them.

You own: OS, runtime, orchestration, scaling, app

They own: Physical hardware, hypervisor, network fabric

Examples: EC2, Azure VMs, GCE, DigitalOcean Droplets

CaaS

Containers as a Service

Managed orchestration — you own images and config

The provider operates the container orchestration control plane; you bring container images and declarative configuration. In 2026, CaaS in practice means managed Kubernetes — EKS, AKS, GKE, OKE, DOKS — plus simpler container runners like ECS or Cloud Run at the lighter end.

You own: Images, manifests, workloads, node sizing

They own: Control plane, etcd, API server uptime

Examples: EKS, AKS, GKE, OKE, DOKS, ECS, Cloud Run

PaaS

Platform as a Service

Push code — the platform does the rest

You hand the platform your application code or a container, and it builds, deploys, routes, scales, and patches without you thinking about any of it. The Heroku model, alive and well in App Platform, Render, Fly.io, and Vercel.

You own: Application code and its configuration

They own: Everything else, on their terms

Examples: Heroku, DO App Platform, Render, Vercel, Fly.io

One rung further up sits serverless/FaaS — individual functions, billed per invocation, with no visible infrastructure at all. It's the logical extreme of the same trade: unbeatable for event-driven glue and spiky workloads, awkward for anything long-running, stateful, or latency-sensitive. Treat it as a tool in the mix, not a destination for the whole estate.

Decision Matrix

Control vs Convenience, Dimension by Dimension

Six questions that actually separate the layers. Read your column top to bottom and see which one describes the deal you want.

OS patching

IaaS

You — every CVE, every reboot window

CaaS

Shared: provider patches the control plane, you own (or auto-manage) nodes

PaaS

Provider, invisibly

Scaling

IaaS

You script it or buy tooling

CaaS

Declarative — HPA and cluster autoscaling are built into the platform

PaaS

A slider, within the platform's limits

Portability

IaaS

High in theory, but every environment is hand-built

CaaS

Highest in practice: the Kubernetes API is the same on all five managed services and on your own metal

PaaS

Low — buildpacks, addons, and routing are platform-specific

Compliance surface

IaaS

Everything is your audit scope

CaaS

Documented shared-responsibility line; policy-as-code and audit logging are first-class

PaaS

You inherit the platform's certifications and its limits

Cost shape

IaaS

Cheapest per compute hour, priciest in engineering time

CaaS

Node pricing plus a small control-plane fee; efficiency scales with bin-packing

PaaS

Premium per unit of compute — negligible at small scale, punishing at large

Team skills

IaaS

Systems administration depth across the whole stack

CaaS

Platform engineers who know Kubernetes — a hiring pool that keeps growing

PaaS

Any developer who can push to Git

When CaaS Wins

Managed Kubernetes Is the Modern CaaS Default

The middle rung earns its place whenever workloads are steady, regulated, portable, or GPU-shaped — which describes most of the enterprise estate.

Steady, serious workloads

Once services run around the clock and count in the dozens, CaaS bin-packing and right-sizing beat PaaS per-unit pricing by a wide margin — and you get the knobs to prove it.

Compliance requirements

HIPAA, SOC 2, PCI, FedRAMP work needs network policies, admission control, audit logs, and a shared-responsibility line auditors already understand. Managed Kubernetes has all four; most PaaS offerings cap out earlier.

Portability across clouds and metal

The same manifests run on EKS, AKS, GKE, OKE, DOKS, or your own RKE2 cluster. That's the Managed Clouds row of our Metal-to-Model architecture — one operating model everywhere, exit costs near zero.

AI and GPU workloads

GPU scheduling, device plugins, and inference serving are Kubernetes-native problems with Kubernetes-native answers. There is no serious PaaS story for a GPU fleet — this is where CaaS is simply the only mature option.

This is the Managed Clouds row of our Metal-to-Model platform: EKS, AKS, GKE, OKE, and DOKS operated through one GitOps workflow, interchangeable with on-prem RKE2 and bare metal. The orchestration layer being open source is what keeps every one of those arrows reversible.

When PaaS Wins

Sometimes the Right Answer Is the Simple One

An honest comparison cuts both ways. There are workloads where a cluster is overkill, and pretending otherwise is how consultancies lose trust.

Small teams shipping standard web apps

If the whole product is a web app, a worker, and a database, a PaaS deploys it today with zero platform staff. That speed is real value, not a compromise.

Speed over control

Prototypes, internal tools, marketing sites — anything where a deploy pipeline matters more than infrastructure ownership. We practice what we preach: thnkbig.com itself runs on DigitalOcean App Platform, because a static marketing site doesn't deserve a cluster.

Bridging stages

PaaS first, CaaS when the bill or the requirements say so, is a legitimate sequence — if you keep your app twelve-factor clean, the migration is mostly packaging, not rewriting.

The Traps

Where Each Layer Hurts You Later

Both failure modes are predictable years in advance — which means both are avoidable at design time.

The PaaS scale trap

PaaS pricing is a convenience premium on every compute unit, and it compounds: what costs little at two dynos is a budget line at two hundred. Worse, the platform-specific pieces — buildpacks, addon data stores, routing behavior — are exactly the pieces that resist migration. Teams rarely leave a PaaS because they want to; they leave when the invoice forces the conversation, at the moment they have the least slack to re-platform.

The DIY-IaaS Kubernetes trap

The opposite failure: self-managing Kubernetes on raw VMs to save the managed-service fee, without platform engineering to back it. Clusters are easy to create and hard to operate — upgrades, etcd health, certificate rotation, and CNI debugging land on a team that signed up to ship features. That's the day-2 stall, and it's why the control plane is the one layer worth delegating even for teams that own everything else.

The day-2 problem — what happens after the cluster exists — is the deciding factor more often than any pricing sheet. We wrote up what that actually involves in the Kubernetes orchestration guide.

FAQ

CaaS and PaaS Questions, Answered

CaaS is a cloud service model where the provider operates the container orchestration layer — the control plane — while you supply container images and declarative configuration. You keep control of what runs, how it scales, and how it's networked; the provider keeps the orchestrator itself alive. In current practice, CaaS overwhelmingly means managed Kubernetes services such as EKS, AKS, GKE, OKE, and DOKS.
Kubernetes itself is an open-source orchestrator, not a service — but managed Kubernetes is the definitive CaaS. When AWS, Azure, Google, Oracle, or DigitalOcean run the control plane and you bring workloads, that's Containers as a Service in exactly the sense the term was coined for. Self-managing Kubernetes on your own hardware is the same technology consumed as infrastructure instead of as a service.
Ownership of the middle of the stack. On a PaaS you hand over application code and give up control of runtime, scaling behavior, and networking in exchange for zero operational work. On a CaaS you keep that control — you define the containers, resources, autoscaling, and network policy — while delegating only the orchestrator's control plane. PaaS optimizes for developer convenience; CaaS optimizes for operational control with the undifferentiated parts removed.
PaaS is the model where the platform takes your source code or a container and handles build, deployment, routing, scaling, TLS, and patching end to end — Heroku made the pattern famous, and platforms like DigitalOcean App Platform, Render, and Vercel carry it today. It's the fastest path from code to URL that exists, at the price of platform-specific behavior and a per-unit cost premium that grows with scale.
Usually CaaS, on managed Kubernetes. Regulated workloads need demonstrable controls — network segmentation, admission policy, audit logging, encryption posture — and a shared-responsibility model auditors can reason about. Managed Kubernetes provides those as first-class primitives and keeps workloads portable if data-residency or sovereignty requirements later force a move on-premises. Pure PaaS can pass audits for narrow scopes, but teams tend to outgrow its control surface mid-certification.
Yes, and it's often the right sequence. The migration cost depends almost entirely on discipline while you're on the PaaS: keep the app twelve-factor, keep state in external services rather than platform addons, and ship as a container rather than a buildpack if the platform allows it. Do that, and the later move to managed Kubernetes is packaging and pipeline work measured in weeks — not a rewrite.
Related

Related Resources

Kubernetes Orchestration: The Complete Guide

What the orchestrator actually does — scheduling, self-healing, autoscaling — and how to run it well.

Sovereign AI Guide

When the answer is below IaaS: your own metal, your own models, inside your perimeter.

Kubernetes Consulting

Platform engineers for EKS, AKS, GKE, OKE, DOKS, and self-managed RKE2.

Cloud Migration

Moving between layers of the ladder — PaaS to CaaS, IaaS to managed Kubernetes — without downtime.

Cost Optimization

Right-sizing, bin-packing, and the cost shape of each service model at your scale.

Case Study: Delta Data on AKS

A fintech's managed-Kubernetes platform assessed and hardened — CaaS in regulated practice.

Choosing a Layer Is Choosing an Operating Model

The most expensive mistake in the IaaS-vs-CaaS-vs-PaaS decision isn't picking the wrong acronym — it's treating the choice as a procurement question when it's an operating-model question. A PaaS quietly decides how your team deploys, what your architecture can look like, and what your exit costs are. A raw-IaaS build quietly decides that you're now in the infrastructure business, whether or not you staffed for it. The service model you choose is the org chart you'll grow into.

That's why Containers as a Service — in practice, managed Kubernetes — has become the center of gravity for mid-market and enterprise platforms. It's the one rung where the interface you operate against is an open standard rather than a vendor surface. The same manifests, the same GitOps pipeline, and the same platform-engineering skills apply on EKS, AKS, GKE, OKE, DOKS, on-premises RKE2, or bare metal with GPUs in it. Delegate the control plane, keep the operating model: that's the deal, and for workloads that matter, it's usually the right one.

The practical sequence we recommend: classify workloads by the pager question — who should own the OS, the orchestrator, the app. Put genuinely simple things on a PaaS without guilt. Put the steady, regulated, and GPU-shaped estate on managed Kubernetes with real platform engineering behind it. And reserve raw IaaS for the cases that earn it, like sovereign AI infrastructure where the hardware itself is the point. Layers are tools; the architecture is knowing which workload belongs on which rung.

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