Someone on your team shipped an app. Now you own it.
We take apps built with AI coding tools and give them what production requires: authentication, supported hosting, a firewall, a real deployment pipeline, and backups. Fixed scope, defined endpoint.
Talk to an Expert →The prototype became load-bearing
An employee described what they needed to an AI coding tool and had something working by the end of the afternoon. It solved a real problem, so a business team started using it. Then another team did. Now it holds company data, and the person who built it isn't an engineer.
None of the decisions behind that app were made with production in mind, and the risk sits with IT. IT teams call us in for this more often each month.
Three steps before anyone touches infrastructure
- Review the code first. Before any conversation about hosting or budget, an engineer reads the code: how it handles user input, where credentials and API keys live, whether database calls are parameterized, and whether there's any separation between layers.
- Size the gap to production. We map what exists against what production requires. Sizing it accurately is what keeps the work from turning into an open-ended engagement.
- Quote a fixed scope tied to the outcome. The review turns into a fixed scope with a defined endpoint. No hourly meter running against work nobody has defined.
Five things almost every AI-built app is missing
Which tool generated the app barely matters. The gaps are the same.
Security
- Authentication: AI tools often produce a shared login, a hardcoded key, or no auth at all. Azure App Service authentication with Microsoft Entra ID signs users in without a custom auth layer.
- A web application firewall: Azure Web Application Firewall filters common exploit traffic like SQL injection and cross-site scripting before it reaches code nobody has reviewed.
Operations
- Supported hosting: moving from the tool vendor's hosting to Azure App Service or Azure Container Apps brings the app inside the monitoring and cost controls you already run.
- A deployment pipeline: a build step, automated tests, and a controlled release through Azure DevOps or GitHub Actions, with rollback.
- Backup and recovery: scheduled backups of the underlying database and a restore you've tested.
The full breakdown is in our post on why vibe-coded apps need a path to production on Azure.
Our take: don't ban the tools, give them a road to production
More people can now start software than ever before. Good. Banning the tools pushes the work somewhere you can't see it. The hard part hasn't changed: authentication, security, deployment, and an architecture someone can operate at 2 AM. That's where experience matters, and it's the part AI tools skip.
So we treat each app as a signal. If one team built something with an AI tool, others are using tools you haven't inventoried, on data you haven't scoped. An approved tools list and an acceptable use policy, designed with your team, turn that from a recurring surprise into something you manage. The tenant-side controls are covered by our AI Guardrails Assessment.
Why CloudServus
- Top 1% Microsoft Solutions Partner, with a Digital & App Innovation designation on Azure
- Azure Expert MSP, independently audited by Microsoft and held by fewer than 1% of partners
- 1,600+ engagements delivered, including app modernization and security hardening
- Fixed scope, not an open meter. You get a defined endpoint before work starts.
Frequently asked questions about vibe-coded apps
What does vibe coding to production mean?
It means taking an app someone built with an AI coding tool and adding what production requires: authentication, hosting your IT team can support, a web application firewall, a deployment pipeline with testing and rollback, and backups with a tested restore. The app's logic already works. What's missing is everything around it.
Is vibe-coded software a security risk?
It can be. AI coding tools rarely add authentication, input validation, or secret management unless someone asks, and the person asking usually isn't a security engineer. The risk grows once the app holds real company data. A code review finds the specific issues, such as credentials in source or unparameterized database calls, so they can be fixed.
Does the AI-generated code get thrown away?
Usually not. The application logic typically stays, because it already does the job the business relies on. The work adds the missing production layers around it. Where the review finds unsafe code, that specific code gets fixed and we explain why.
How long does it take to get a vibe-coded app production-ready on Azure?
The review and sizing steps typically finish within one to two weeks, followed by a fixed-scope quote. The build itself depends on what the app touches, which is exactly what the sizing step establishes before anyone commits to a date.
Should we just ban AI coding tools?
Banning them moves the activity somewhere you can't see it. A better position is a named list of approved tools, clear rules on what data can go into them, and a path to production for anything a team comes to depend on.
Find out what stands between that app and production
No slide decks. Senior Microsoft engineers, real numbers, and a fixed scope with an endpoint.
Talk to an Expert →