Build with Claude Code, Deploy with Qovery

Key points:

• AI tools like Claude Code let you build full apps in minutes - but deploying them still requires Dockerfiles, CI/CD, Kubernetes manifests, DNS, SSL, and secrets management
• Qovery Skill bridges the gap: one install command gives AI agents the ability to deploy to production Kubernetes
• Every action - human or AI - passes through the same RBAC, policies, and audit trail
• The result: zero-to-production in a single conversation, with enterprise-grade governance

Last month, I built a full-stack app with Claude Code in about 20 minutes. The code was clean, the tests passed, the PR was ready. Then I spent the next 2 hours fighting with Dockerfiles, Kubernetes manifests, environment variables, DNS records, and SSL certificates to get it running in production.

20 minutes to build. 2 hours to deploy. Something is very wrong with this picture.

The New Bottleneck

AI coding tools have genuinely changed how we write software. I'm not exaggerating - tools like Claude Code, Cursor, and GitHub Copilot eliminated an enormous amount of friction from the development process. You describe what you want, the AI writes the code, runs the tests, and creates the PR. It's fast. It works.

But here's the thing nobody talks about: the bottleneck has shifted. Writing code is no longer the hard part. Deploying it is.

After git push, developers still face:

• Writing and maintaining a Dockerfile
• Setting up CI/CD pipelines
• Creating Kubernetes manifests (or learning Helm)
• Configuring DNS, SSL, and load balancers
• Managing environment variables and secrets
• Setting up monitoring and logging
• Handling database provisioning and migrations

The code is no longer the hard part. The infrastructure is.

And for most developers - especially those who just "vibe coded" their app into existence in 20 minutes - this wall is where momentum dies.

What Is Claude Code?

For those unfamiliar: Claude Code is Anthropic's agentic AI coding tool. It lives in your terminal, reads your entire codebase, writes code across multiple files, runs tests, and creates PRs. It's used by teams at Spotify, Shopify, Figma, and Stripe.

What makes it different from a simple autocomplete is that it's an agent. You give it a task - "add user authentication to this app" or "fix the failing tests in the payments module" - and it plans the approach, makes changes across multiple files, and verifies its own work.

Install it in one command:

curl -fsSL https://claude.ai/install.sh | bash

What Is Qovery?

Qovery is an Enterprise Kubernetes Management Platform. It runs on your own AWS, GCP, or Azure account - your workloads never leave your VPC.

Here is what it handles for you:

• Deployment: any language, any framework, from Dockerfile or pre-built container
• Managed databases: PostgreSQL, MySQL, MongoDB, Redis - one-click setup with automated backups
• Preview environments: auto-deploy on git push, environment-per-PR
• SSL, DNS, load balancing: all automatic
• Cost optimization: Karpenter, spot instances, sleep mode for dev/staging environments
• Compliance: SOC 2 Type II, HIPAA, GDPR, DORA

But the part that matters for this article is what we released recently: the Qovery Skill for AI Agents.

The Bridge: Qovery Skill 🚀

The Qovery Skill is a single install command that gives any AI coding agent - Claude Code, Cursor, Codex, OpenCode, Gemini CLI, and 20+ others - the ability to deploy applications through Qovery.

curl -fsSL https://skill.qovery.com/install.sh | bash

That's it. Once installed, your AI agent can:

• Analyze your codebase and create a Dockerfile if one doesn't exist
• Provision databases (PostgreSQL, Redis, etc.) automatically
• Set up environment variables and secrets
• Deploy to production on your Kubernetes cluster
• Monitor the deployment and troubleshoot issues

The whole flow looks like this:

Zero to production. One conversation. No YAML. No kubectl. No context switching between 5 different tools.

Why Platform Teams Love This

I know what you're thinking: "Great, so now AI agents have free rein over my production infrastructure?"

No. That's exactly the point. Every action the AI agent takes goes through the same governance pipeline as a human developer.

Here is what makes this different from an AI agent running kubectl apply or aws deploy directly:

• RBAC and policy enforcement. The AI agent can only do what its Qovery permissions allow. If your policy says "no deploying to production without approval," the AI agent follows that rule too.
• Full audit trail. Every deployment is logged, attributed, and reviewable. You can see exactly what the agent deployed, when, and what changed.
• Environment isolation. The agent deploys to dev by default. Production requires explicit configuration and the right permissions.
• Scoped and reversible. Every action can be rolled back. No "oops, the AI agent deleted the database" scenarios.

Instead of 5+ fragmented systems (CI/CD, container registry, kubectl, secrets manager, DNS provider, monitoring tool), everything routes through one API. Fewer tools means fewer tokens wasted, fewer misconfigurations, and complete control.

Agents ship fast. Guardrails keep them safe.

The Full Setup

Here is the complete workflow - from zero to "AI agent deploying to production with governance":

curl -fsSL https://claude.ai/install.sh | bash

# 2. Install the Qovery Skill
curl -fsSL https://skill.qovery.com/install.sh | bash

# 3. Open Claude Code in your project
claude

# 4. Deploy
> "Deploy this to production on AWS with Qovery"

Four commands. That's the entire developer experience.

Here is the full workflow in action - deploying via OpenCode with Claude Opus 4.6:

For platform teams, the setup is equally straightforward. You configure your Qovery organization with the right RBAC, policies, and environment structure once. After that, every developer (and every AI agent) operates within those boundaries automatically.

What This Means for Your Team

Let me be direct about what I think this changes:

• Developers go from idea to production without learning Kubernetes, Docker, or CI/CD. The AI handles the code. Qovery handles the infrastructure. The developer handles the product.
• Platform teams keep full control. The same RBAC, policies, and audit trail apply whether the deployment comes from a human or an AI. No Shadow IT. No rogue deployments. No surprises.
• CTOs get speed without sacrificing governance. Teams ship faster, cloud costs stay controlled, compliance requirements are met.

The person writing the code is the same one deploying it. No handoffs, no tickets, no "it works on my machine."

The Other Side of the Coin

I should mention - this kind of power comes with responsibility. AI agents deploying code without proper guardrails is genuinely dangerous. We've already seen AI agents delete production databases, grant overpermissioned OAuth tokens, and compromise supply chains.

If you're interested in the security side of this story, I wrote about it in detail: Shadow IT Is Back - And Vibe Coding Made It 10x Worse. The TL;DR: the solution to Shadow IT in the AI era is not banning AI tools. It's routing them through a governed platform.

Wrapping Up

AI tools eliminated the "writing code" bottleneck. Qovery eliminates the "deploying code" bottleneck. The Qovery Skill bridges them so that a single prompt goes from idea to production - with enterprise-grade guardrails at every step.

Try Qovery free and install the Qovery Skill to get started. Or come chat with us on Discord - we'd love to see what you build.