We’re Vibe Coding in 2026. Why Are We Still Deploying Like It’s 2018?

Source: Dev.to

The way we write code has changed more in the last few years than it did in the decade before that. Most of the day‑to‑day development now looks very different from how it used to.

We build faster, iterate more, and rely heavily on AI‑assisted workflows. Vibe coding isn’t a buzzword anymore—it’s how a lot of us actually work.

But every time you finish coding and move toward deployment, it feels like you’ve stepped back in time.

The Way We Write Code Has Fundamentally Changed

Today, building software is no longer the slow part. AI tools help generate boilerplate, refactor logic, and speed up experimentation. You describe what you want, tweak the output, and move forward. The feedback loop is tight, fast, and creative.

This has changed how developers think:

• We build in smaller iterations
• We deploy more often
• We expect tools to stay out of the way

Coding feels lighter. Flow matters more than ever.

Deployment Still Breaks That Flow

Then comes deployment. Suddenly, you’re no longer building—you’re configuring.

• Thinking about build commands, environment variables, service boundaries, and runtime behaviour
• Watching logs, retrying failed deploys, and switching context constantly

Nothing here is impossible, but it’s slow in a way that feels unnecessary. In a vibe‑coding workflow, this context switch is painful. It breaks momentum and turns shipping into work instead of a natural extension of building.

The Platforms We Use Still Assume a 2018 Workflow

Most modern teams rely on platforms like Netlify and Render. Netlify made frontend deployment dramatically easier. Render simplified backend infrastructure compared to raw cloud providers. Both reduced friction that used to slow teams down.

However, they were designed around a different assumption: developers want to stay involved in deployment. Using these platforms usually means:

• Defining how builds run
• Managing environment variables
• Configuring services
• Handling deployment failures manually
• Making scaling and resource decisions

They removed servers, but they didn’t remove responsibility. That made sense in 2018; it feels increasingly out of place in 2026.

Why Vibe Coding Exposes the Gap

Vibe coding works because it minimizes friction. You stay in a creative loop where tools respond to intent instead of configuration. The moment you have to slow down and think about setup, that loop breaks.

Deployment is now the slowest part of shipping—not because the tools are bad, but because they still require developers to think like operators when they just want to build.

This gap didn’t matter as much when deployments were infrequent. It matters a lot when you’re deploying daily—or multiple times a day.

Key questions arise:

• If AI can help write code, refactor logic, and speed up development, why doesn’t it handle deployment too?
• Why are we still manually describing how our applications should run, scale, and behave in production?

It isn’t a tooling problem; it’s a mindset problem. Development tools moved toward intent‑driven workflows, while deployment tools largely didn’t.

What AI‑Driven Deployment Changes

AI‑driven deployment flips the model. Instead of asking developers to configure every step, the platform focuses on intent:

• Connect your code
• The system automatically figures out how to build, deploy, scale, and operate it

Resulting benefits:

• No pipelines to maintain
• No services to wire together
• No upfront infrastructure decisions

Deployment becomes a background process, not a task.

Where Kuberns Enters the Picture

Kuberns is built around the idea that deployment responsibility should move away from developers entirely.

Simple workflow:

1. Connect your GitHub repository
2. Click Deploy

From there, the platform handles:

• Runtime and build detection
• Deployment
• Scaling based on real traffic
• Monitoring and reliability
• Cloud infrastructure management

You don’t manage how deployment works, tune scaling rules, or think about cloud resources—deployment just happens.

Why This Matters Beyond Convenience

When deployment is automated end‑to‑end:

• Developers stay in flow
• Operational work drops dramatically
• Fewer decisions lead to fewer mistakes
• Cloud resources are used more efficiently

In practice, teams often see around 40 % savings on AWS costs, not through discounts but by eliminating over‑provisioned and idle infrastructure. More importantly, deployment stops being a separate phase and becomes part of the natural development loop.

The Future of Deployment Feels Inevitable

Traditional PaaS platforms aren’t disappearing overnight. They still work well for teams that want control and are comfortable managing deployment details.

But for teams building fast, shipping often, and embracing AI‑assisted development, the old deployment model feels like friction that no longer needs to exist.

We’re already vibe‑coding in 2026. Deployment will eventually catch up. When it does, it won’t be about better configuration screens or more options—it will be about removing the need to think about deployment at all.

If deployment still feels like a slowdown in your workflow, that’s probably not a tooling failure. It’s a sign that the model itself needs to change.