Cloud-Native, IaaS, PaaS and SaaS Explained

Cloud is not a place in the sky. It is the question of how much of the machine you run yourself and how much someone else does.

I work at the intersection of product, GTM and AI and I am building my 10th company. The moment AI agents run inside your product team, this question stops being theory. It decides where your agents work and how fast.

What you take away here:

• What IaaS, PaaS and SaaS actually mean, in one sentence per layer.
• Why the lines between them have long since blurred.
• Why cloud architecture matters when agents run on your side.

My thesis: the three models are three layers of the same stack. You just need to know which one you run yourself.

🧨 Three layers, one stack

The official definition comes from NIST. In daily work, three simple sentences are enough.

IaaS, Infrastructure as a Service. The bottom layer. You rent only the hardware: compute, storage, network, virtualized in a data center. You install the operating system and everything above it yourself. You rent IT infrastructure and maintenance, nothing else.

PaaS, Platform as a Service. The middle layer. Here it is not the IT administrator who is called upon, but the developer. The provider supplies the environment as a framework and sets the programming languages, interfaces and data storage. You control only your own code and data, not the infrastructure beneath.

SaaS, Software as a Service. The top layer, it builds on the other two. You do not buy the software and you do not install it. You rent it as a service. Usually an internet-capable device and a browser are enough. Cheap hardware on your side, full compute power in the data center.

🛠️ Why the lines blur

The clean layer cake is theory. In practice the lines have been blurring for years. Even before Azure offered virtual machines in 2012, it was often no longer clear where IaaS ends and PaaS begins.

Real projects do not ask about the model. They bundle capabilities: an API here, a component there, a service on top. It is not IaaS or PaaS. It is IaaS and PaaS and SaaS, often in the same project. On top of that comes the hybrid approach, which blurs even the line with your own hardware on premises.

At first you still had to explain and justify PaaS. Soon it was only about real development projects. The maturity around hybrid cloud grew year after year. That is where we stand today: not one model, but a mix you assemble on purpose.

🤖 Why this matters in the age of agents

This is exactly where it gets concrete for me. I build AI-native product teams. I call it Get Multiplayer: how people and AI agents work together. A hyperlean team, a few pros plus agents running around the clock. Three instead of thirty.

These agents run somewhere. Whether on IaaS, PaaS or connected as SaaS decides latency, cost and data control. If you have to keep your code, your Jira and your data in the EU, then the layer choice is not a technical question but a compliance question.

On top of that comes context. An agent is only as good as its foundation. That foundation is the Context Engine: your company's business and code context. Where that context lives and how the agents reach it is a question of cloud architecture. Without clean context it is garbage in, garbage out.

🎢 Highs, lows, warning

✅ What works: the layers as a mental model. For every service ask: which layer do I run myself, which one does someone else run. That clears up most of the confusion.

❌ What does not work: treating the models as rigid boxes. Reality is mixed, hybrid and blurred. Wait for the clean cake and you plan past practice.

⚠️ Warning: do not let your cloud architecture happen by accident. The moment agents run on your side, it determines speed, cost and where your data sits.

Cloud is not a place in the sky, it is a decision about what you run yourself. In the age of agents that decision gets more important, not less. If you want to put agents in your product team that know your context, take a look at teklens.ai.