Serverless Is Like Teenage Sex – Unpacking the Hype and Reality

Serverless is like teenage sex

Big data is like teenage sex: Everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it.

AI is like teenage sex: Everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it.

Serverless is like teenage sex: Everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it.

From this analogy we can draw several observations:

Everyone is talking about Serverless.

Almost no one knows how to actually implement Serverless.

People assume others are heavily investing in Serverless.

Therefore everyone claims they are doing Serverless.

Serverless, like many buzzwords such as micro‑services, lacks a precise definition and a factual standard. In the cloud world, Kubernetes has become a de‑facto standard, and for Java developers Spring Boot / Spring Cloud serve a similar role.

A factual standard means a methodology that has been widely adopted and proven in real‑world deployments. Its adoption usually requires two conditions:

It is open (open‑source), avoiding vendor lock‑in.

There are many successful cases that have validated it in critical business systems.

Today the Serverless/FaaS space still does not have such a concrete standard.

Serverless Vision

Google Trends shows the rising interest in Serverless since AWS launched Lambda in 2016.

The vision is not a server‑less world; servers still exist but are abstracted away so developers no longer see them directly.

A paper from UC Berkeley illustrates this vision: today’s way of operating cloud resources is comparable to early programmers writing assembly code.

High‑level languages like Java, Go, and JavaScript, together with their compilers/VMs, translate business logic into machine code, sparing developers from low‑level assembly.

In the cloud era, the basic resource unit has shifted from CPU/memory to containers, functions, distributed queues, and caches. The “cloud OS” role is now largely filled by the Kubernetes ecosystem, while a true “cloud compiler” that translates high‑level code directly into cloud resources is still missing.

Write locally, compile to the cloud.

What the Industry Is Doing

Recent developments in the Serverless space can be grouped into three layers, from bottom to top: resource layer, DevOps layer, and framework/runtime layer.

The resource layer focuses on the lifecycle management of resources such as containers, and on security isolation. This is the domain of Kubernetes, Firecracker, gVisor, and similar projects.

The DevOps layer deals with change management, traffic routing, auto‑scaling, and event‑driven integration. Its goal is to eliminate operational overhead (NoOps). Among the many vendor FaaS offerings, the open‑source Knative project stands out for its comprehensive model and rapid ecosystem growth, and it may become a universal standard similar to Kubernetes.

The framework and runtime layer, especially in the Java world, focuses on reducing startup latency (e.g., GraalVM) and avoiding vendor lock‑in (e.g., Spring Cloud Function).

Where the Real Needs Lie

Products evolve through four stages: startup, mature, platform, and cloud‑product phases. As teams grow, isolation becomes critical—first for stability, then for security. Increased isolation, resource cost, and scheduling demands become essential concerns.

Help developers hide cloud‑resource management; developers dislike writing YAML like assembly.

Provide a pure asynchronous, event‑driven model that matches the distributed nature of the cloud.

Avoid vendor lock‑in by being cloud‑agnostic.

Combine cloud‑resource management with mainstream programming paradigms (e.g., object‑oriented design) to deliver a great developer experience.

Conclusion

Serverless appears attractive, but its practical adoption reveals deep complexities across engineering, user expectations, and future predictions. Understanding these challenges and the underlying needs for stability, security, and cost‑effective resource management is essential for anyone looking to build or adopt Serverless solutions.