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Year: 2026

Low-Code vs. Vibe Coding: When to Use Each in Your Business

Low-code vs. vibe coding: Both turn a plain-language request into a working app. Low-code assembles the app from platform templates; vibe coding has AI write the code from a prompt.

Somewhere in your company, someone has already vibe coded an app. Maybe an analyst got tired of a 15-tab spreadsheet and asked an AI tool to build a tracker instead. Maybe a manager wanted a dashboard that IT didn’t have time for, and just asked AI to build one for them.

If you run IT, that should impress you and worry you at the same time.

What Is Vibe Coding?

Vibe coding means describing the app you want to an AI tool and letting it build one for you, usually without reading the code underneath. AI researcher Andrej Karpathy coined the term “vibe coding” in early 2025 for a style of building where you “fully give in to the vibes” and “forget that the code even exists.” By November, Collins Dictionary had named it Word of the Year.

What Is Low-Code?

Low-code needs less introduction. Low-code platforms let your team build full web applications from templates and configuration instead of writing every line by hand. It’s been the standard answer to the app backlog for more than a decade.

What’s the difference between vibe coding and low-code? Does vibe coding replace low-code tools? In this article, we’ll answer those questions and more.

What Vibe Coding Does Well

I’m not going to spend this article telling you vibe coding is hype. It isn’t.

According to the Stack Overflow 2025 Developer Survey of 49,000 developers, 84% use or plan to use AI tools in their work, and 51% use them daily. And that’s just the professional developers. The bigger trend is what everyone else is doing: People with no programming background are now building applications by simply chatting with AI.

For simple, personal apps, vibe coding can be great. For instance, if your users are doing things like:

  • Replacing spreadsheets. Someone replaces a manual spreadsheet workflow with a small app that saves them an hour a week.
  • Prototypes. A team mocks up a working demo of an idea instead of writing a requirements doc about it.
  • Building one-off utility tools. For example, scripts that rename files, reformat reports, or merge two lists.

Notice what these have in common: If the app fails, almost nothing happens. Nobody’s data leaks. No process stops.

That’s what vibe coding is great for. Automating small tasks that can save hours for the user.

Where Should You NOT Use Vibe Coding?

Vibe coding is fine for apps where nothing bad happens if they break. However, once an application connects to your database or handles customer data, vibe coding can create problems.

Why? Think about everything that happens behind the scenes in a secure business application. It connects to the database with some set of credentials. It decides who sees what, so a warehouse clerk and a CFO don’t get the same data. It writes records other systems depend on. An AI tool will generate all of that, but it has no idea which parts it got wrong.

There’s a growing amount of security research that highlights the risks of vibe coding. When Wiz examined apps built on vibe-coding platforms, researchers found passwords hardcoded in JavaScript files, API keys visible in the browser, and internal tools sitting on the public internet with no authentication at all. That’s what these tools produce by default when nobody reviews the output.

More importantly, when things go wrong at the database level, they go wrong fast. In July 2025, an AI coding agent on Replit deleted a production database during a session run by SaaStr founder Jason Lemkin, despite explicit instructions to freeze all changes. The agent then reported that restoration was impossible. It was wrong about that too; backups existed. Replit’s CEO called the incident “unacceptable” and announced automatic separation between development and production environments. That story got publicity because it happened to someone with an audience. But, this is happening more and more inside companies.

What Happens When Vibe-Coded Apps Reach Production

The more you dig into the research, the worse it gets.

According to Veracode’s March 2026 GenAI Code Security Report, which tested more than 100 large language models, 45% of AI-generated code contained known security flaws. That number has stayed essentially flat for two years despite much better models. The models write cleaner code, and they keep making the same security mistakes.

You can see the same thing in deployed apps. In October 2025, researchers at Escape.tech scanned about 5,600 publicly deployed vibe-coded applications and found more than 2,000 high-impact vulnerabilities, over 400 exposed secrets, and 175 instances of exposed personal data, including medical records. These were live apps, findable within hours.

And this has already crossed from research into incident reports. Aikido Security’s 2026 State of AI report found that 20% of organizations have suffered a serious incident traced directly to AI-generated code, and another 49% have hit minor ones.

I know what you might be thinking: “Our developers review everything before it ships.” Good. Then your developers aren’t vibe coding; they’re using AI with a review process, which is exactly where it belongs. The apps that cause trouble get built without code review, by whoever has a prompt window open.

How Is Low-Code Different Than Vibe Coding?

On the surface, low-code and vibe coding seem similar. They both create applications quickly. The big difference between the two is all the ‘stuff’ around the application that isn’t immediately visible, but still very important. For instance:

  1. Application architecture: A vibe-coded app is generated fresh, with all of the code written new for that one app. A low-code platform assembles each app from the same underlying architecture it uses for every app. It’s using code that’s been through years of use. Your developers configure what the app should do, but the underlying architecture is prebuilt and tested.
  2. Security: With low-code, security exists at the platform level. You set up your rules once, your sign-on and who sees which data, and every app inherits them. Database credentials work the same way, configured once rather than written into each app’s code.
  3. Deployment: With low-code, apps move from development to production through a process IT manages, so nothing goes live until someone moves it there.
  4. Maintainability: With vibe coding, an application built today could be built differently than one put together a couple of weeks ago. With low-code, every app comes out of the same build process so they all share the same structure and standards. That means they’re more easily maintained.
Diagram: One plain-language request, two outcomes. Vibe coding returns raw, unreviewed code you secure, host, and maintain yourself. A low-code platform builds the app on a security model, deployment, and a maintenance path.

None of this makes low-code the automatic answer. You’re buying a license and working inside the platform’s structure, and for a small personal tool, that’s overkill.

When Should You Use Each One?

Vibe coding makes sense when:

  • The app is a prototype, a personal utility, or a spreadsheet replacement
  • A failure inconveniences one person, not the business
  • You want to test an idea before asking for budget, since there’s no license and no setup

A low-code platform makes sense when:

  • The app connects to your live database or handles customer data
  • More than one or two people will depend on it
  • Whoever is on the team next year needs to be able to maintain it
Two lists: Vibe code prototypes, personal trackers, and one-off scripts, where a failure annoys one person. Use a low-code platform when the app connects to the live database, handles customer data, or the whole team depends on it.

One thing to watch out for: Vibe-coded apps can spread beyond their initial target. For instance, maybe someone vibe codes a simple spreadsheet tracker. It gets shared around the department. A department leader sees it and wants it connected to the ERP. Now the throwaway app touches live data, and it was built with none of the things business infrastructure needs.

Low-Code + Vibe Coding: The Best of Both Worlds?

You may not have to choose between vibe coding and low-code. Many modern low-code platforms now have AI built in. You describe what you want in plain language, just like vibe coding.

The difference? The application is built inside the platform’s structure. Security is already handled. So is deployment. You create just as fast, wrapped in an architecture your team can secure and maintain.

I’d like to touch on that last point a bit more, because maintenance is often overlooked. Yet, this is where companies with a lot of vibe-coded apps will run into problems.

A vibe-coded app is a one-off build. The AI writes it however it wants that day, based on whichever model it used. Build 30 apps that way, and you’re maintaining 30 different codebases. GitClear looked at 211 million lines of code and saw this happening: Once AI assistants took hold, copy-pasted code jumped from 8.3% to 12.3%, and refactoring fell by more than half. Gartner predicts prompt-to-app development will increase software defects by 2,500% by 2028. Even if the real number is far lower, guess who fixes them?

Comparison: 30 vibe-coded apps produce 30 mismatched codebases where every fix starts from scratch; 30 low-code apps share one structure anyone on the team can maintain.

Low-code apps don’t have that problem. Every app shares the same structure. The app your developer built three years ago works just like the one your new hire built today. Anyone on the team can open either one and know where to look.

Not All Low-Code Tools Are the Same

There’s a lot of myths floating around about low-code platforms as a whole. Many believe that low-code locks you in, the code can’t run anywhere else, and if you stop paying, your apps stop working.

For some platforms, those complaints are true. Some run only in the vendor’s cloud, so your apps and your data live on someone else’s infrastructure. Some meter pricing per user, so every successful app raises your bill. Some generate apps in a proprietary format that only runs on their platform.

But, every low-code platform is different. Those complaints aren’t universal. Those are choices vendors make, and not every vendor makes them.

Here’s a good question to ask before licensing a low-code tool: If this vendor disappeared tomorrow, would my apps keep running? Look for a platform that installs in your environment, on your servers or your cloud. Look for one that builds over the databases you already have instead of copying data into theirs. And look for ownership terms where what you build is yours, permanently, no matter how many people use it.

That’s the standard I’d hold any low-code vendor to, including us.

m-Power: On-Premise Low-Code You Own

m-Power is a low-code development platform built by mrc, a development software company since 1981 with more than 1,500 customers. m-Power installs in your environment, on-premise or in your cloud, and builds applications directly over your existing databases. You buy it once with a perpetual license, unlimited users and unlimited applications, and everything you build is yours.

The AI side is built in. Your team can build AI assistants, chatbots, and agents that work over your data, where IT controls what data the AI can access, who can use each tool, and which model runs behind it. And every app, AI-assisted or otherwise, comes out of the same build process, so it inherits the same security model and the same architecture as everything else your team has built.

And when something goes wrong or a project outgrows your team, support is staffed by consultants who build with m-Power daily, with a services team behind them for anything bigger.

We’d be glad to show you what other teams have built with m-Power. We can set up a demo that’s specific to your situation. We often build something directly over your data, so you can see exactly how it works in your environment.

FAQ: Vibe Coding and Low-Code Platforms

Is vibe coding safe for business applications?

For personal tools and prototypes where a failure costs nothing, yes. For anything that touches company databases, customer data, payments, or compliance, the evidence says no: Veracode’s March 2026 testing found 45% of AI-generated code contains security flaws, and researchers scanning live vibe-coded apps found thousands of vulnerabilities and hundreds of exposed secrets. Business apps need a security model, not just working code.

What’s the difference between vibe coding and low-code?

Vibe coding generates an app from a prompt and hands you the raw code; everything else, including security, data access, hosting, and upkeep, is your problem. A low-code platform generates apps inside a governed structure, so every app gets the database connection, security model, and maintenance path automatically. Modern low-code platforms include AI, so the build speed is comparable.

Should IT ban vibe coding?

Bans mostly drive it underground; employees will use AI tools either way. It’s better to give employees a managed option: Let people build, but on a platform where IT already controls data access and permissions. Reserve pure vibe coding for prototypes and personal utilities where failure is cheap.

Can you get vibe coding’s speed with IT control?

Yes, that’s what you get from modern low-code platforms. m-Power’s approach: Describe what you need, build it over your existing database, and the platform applies your security model and deployment automatically.

The 10 Best PowerApps Alternatives in 2026, Compared

10 PowerApps alternatives compared on licensing, hosting, customization, and cost

I’ve spoken with quite a few IT teams that started with PowerApps. It was a good move at the time. They were already paying for Microsoft 365, and the first app went well. It’s easy and cheap enough to get started if you’re already in the Microsoft ecosystem.

While everything usually starts out fine, the problems shows up later on down the road. Based on the conversations I’ve had, businesses look for a PowerApps alternative for a few reasons:

Why teams look for a PowerApps alternative

1. The per-user bill climbs. PowerApps is priced per user, per month. That looks reasonable for a pilot. Then the app succeeds, more people need access, premium connectors get added, and the recurring cost grows. The tool you adopted to save money becomes more and more expensive.

2. You hit a customization ceiling. PowerApps handles common scenarios well. The trouble starts when you need to do something custom, or something that wasn’t built into the platform. Teams routinely find that the exact workflow, layout, or logic they need isn’t possible within the platform, and they end up bolting on fragile workarounds to cover the gap.

3. Microsoft lock-in. PowerApps is designed to keep you in the Microsoft ecosystem. That is convenient until you want to leave, at which point the apps, the data in Dataverse, and the connectors do not come with you easily.

How to Chat With Your Database Using AI Data Analytics

Data Analytics with m-Power AI Studio: a user asks for top 10 states by sales and gets a chart from the database

How many report requests are sitting in your queue right now? Maybe Sales wants last quarter’s numbers broken out by state, with a chart for Thursday’s meeting. Or, maybe Operations needs a list of every order that shipped late last week, and why. Each one might be an hour of work, but the problem is that there are forty more behind them.

Meanwhile, your users have discovered AI. Some are asking why they can’t chat with company data the way they chat with ChatGPT. A few, whether you know it or not, are already exporting data and pasting it in.

Both problems have the same fix: Let people ask the database directly (securely, of course).

That’s one thing that m-Power’s AI data analytics assistant aims to solve. It lets business users chat with your data and get back real numbers, analysis, and charts. The AI never connects to your database directly. IT decides which fields it can see, whether or not the query should be read-only, and you pick the LLM.

We just published a tutorial video aimed at showing customers how it works. Here’s the full walkthrough:

AI for Small IT Teams: How to Give People AI Without Losing Control of Your Data

If you run an IT department, you already have an AI problem. Your people are pasting company data into ChatGPT, Claude, and Gemini right now, on accounts you don’t manage (and you can’t see what they’re sending).

What can you do about it? Your goal should be to make the safe path the easiest path. As I’ll explain in this article, you can do that in three steps:

  1. See what’s already being used
  2. Set a few rules a small team can actually enforce
  3. Give people a sanctioned option that’s better than the risky one

This guide walks through all three, and it’s written for the small IT teams that may not have a security department or a bunch of extra money to throw at the problem. If you have two people covering support, infrastructure, and now “the AI strategy,” this is for you.

What to Look For in a Db2 Web Query Replacement

Support for Db2 Web Query was pulled a few years ago, and many companies migrated away immediately. But, at COMMON PowerUP26 this spring, we were surprised by a couple of things:

First, we were surprised at the amount of people who said they were still running Web Query. They’re running without support until they’re forced to migrate.

Second, was the amount of people who tried to migrate and found it harder than they expected. There is a reason some migrations stall, and it’s worth understanding before you look for a Db2 Web Query replacement.

The big reason: Nothing is a true one-to-one swap for Web Query.

Whatever tool you pick, it will not reproduce your reports on its own. You’ll likely need to re-create those reports, and sooner or later you will hit something that the new tool does not handle out of the box.

The Low-Code Vendor Lock-In Test: Five Questions Before You Sign

One of the most common myths I’ve heard about low-code vendor lock-in is that it’s unavoidable. Pick any platform, the thinking goes, and you’ll be locked in within three years. That’s just part of the deal.

The only problem with that assumption: It’s wrong.

Of course, lock-in is real and exists across many low-code platforms. But it isn’t built into low-code as a category. It’s built into how a specific platform is architected. Some platforms create it. Others don’t. The trouble is that the difference is hard to see during a demo, when every vendor sounds open.

The bigger problem is, a vendor may not define “lock-in” the same way you do. The contractual definition (no clause prevents you from leaving) and the operational definition (the cost of actually leaving) are not the same thing. The architectural decisions baked into a platform are what really determine lock-in, and they are the part that doesn’t usually come up in a sales conversation.

Rather than asking about vendor lock-in, the better question is this: If you wanted to take your applications, your data, and your team and walk away, what specifically would break?

Of course, you can’t ask that question outright in a sales call. The vendor doesn’t know the answer in the abstract any more than you do. But you can answer it yourself, by working through five smaller questions. Each one tests something structural about how a low-code platform works that goes beyond what marketing and sales say about it.

Run any platform on your shortlist through these questions, and the answers will tell you exactly what leaving would cost.

The first one is the foundation. Everything else depends on the answer.

Coming in May: Build AI Agents in Your Environment, Governed by Your Own IT Team

In speaking with IT leaders recently, I keep hearing the same thing. Employees are using AI, with or without IT’s blessing.

The problem: They’re using it in risky ways. For example:

  • They’re pasting customer lists into ChatGPT to summarize them.
  • They’re feeding contracts into public chatbots to analyze them.
  • They’re asking AI tools to review company data they exported to a spreadsheet.

Here’s the part that should worry you. Studies keep finding the same thing, including IBM’s 2025 Cost of a Data Breach report: Employees routinely ignore the AI tools their company actually paid for. Why? Because the sanctioned tools are slower, harder to get into, or don’t do what they need. So, they reach for the public ones instead.

In short, Shadow AI is becoming the default.

I talk to IT leaders who feel like they’re in an impossible spot. They’re trying to manage a workforce that’s already using AI, on tools they didn’t approve, against data they’re responsible for, in ways their auditors and privacy team can’t see. Policy memos don’t fix that.

The only thing that fixes it is giving people a sanctioned path that’s easier than the rogue one. IT needs a way to deliver sanctioned AI that’s easy for the users…without giving up control.

That’s exactly what’s coming next in m-Power.

What’s Driving Cloud Repatriation in 2026?

Something interesting is happening in IT departments right now. The same leaders who spent years migrating workloads to the cloud are quietly moving some of them back.

Not all of them. And, I’m not tyring to say that businesses are now rejecting the cloud or anything like that.

However, there’s a growing push for “Cloud Repatriation” because the math stopped working for certain workloads and the governance requirements changed faster than anyone expected.

In case you’re unfamiliar with the term, let’s quickly define it.

What is cloud repatriation? Cloud repatriation is the process of moving applications, data, or workloads out of a public cloud environment (like AWS, Azure, or Google Cloud) and back to on-premise infrastructure or a private cloud. It’s rarely a full cloud exit. Most often, it’s a targeted move of specific workloads where the cost, performance, or compliance profile makes more sense outside the public cloud.

If you’re an IT director or CIO watching your cloud bills climb past projections every quarter, you’ve probably at least considered it. Cloud repatriation is a strategic shift happening across industries. Let’s get into what’s driving it, why AI is accelerating the trend, and how to evaluate what belongs where in your own environment.

What software should NOT be SaaS?

What types of business software should NOT be used on a SaaS model?

That’s something I’ve been thinking about lately as I read more and more cases of rising SaaS costs. The fact is, most businesses are paying more for their SaaS tools than they were last year. Same tools and users, just a bigger bill. 

73% of SaaS companies raised prices in 2025, averaging 14.2% increases. SaaS inflation is running at roughly five times the general rate. CIOs now spend about 9% of their IT budget just absorbing price hikes on software they already have. According to Zylo’s 2025 SaaS Management Index, SaaS spending per employee hit $4,830 last year, up 21.9% from the year before.

For mid-market companies, per-employee spend jumped 40%. Gartner estimates 25% of all SaaS spend is wasted or underutilized. And 78% of CFOs say they’ve been blindsided by hidden fees or price hikes baked into their contracts.

The vendors aren’t subtle about this anymore. A few examples from the last two years:

How to Build Custom Apps Over Your ERP (Without Changing It)

Let me ask you a question: Does your ERP system actually fit your business?

Does it match the way your people work and/or how your business operates day to day? Probably not…and you’re not alone. I talk to IT leaders all the time who tell me the same thing. Their ERP handles the core stuff fine. Beyond that? There are gaps.

Watch the full walkthrough: In this 20-minute video, we build a purchase order form, and approval workflow, all over ERP data (without touching the ERP).

A PowerApps Alternative That Lets You Own the Platform (No Per-User Fees)

If you’re evaluating PowerApps for the first time in 2026, the pricing just changed.

I know…SaaS pricing changes all the time. So what? The business world has gotten used to it.

But here’s the thing. When you’re dealing with a low-code platform that creates and runs your business applications, it’s different. Your business often relies on the applications that it’s created. A pricing change can have major implications for everything you’ve built and everyone who depends on it.

I want to dig into this idea, using PowerApps’ recent change as an example. Is SaaS really the best approach for low-code?

Shadow AI Is the New Shadow IT (and Better Policies Won’t Stop It)

Something is happening in your organization right now that you probably don’t have full visibility into.

It’s happening in organizations across the globe.

Business analysts are building reporting dashboards with AI tools, connected to live company data, with no IT involvement.

Finance staff are pasting budget projections into consumer AI platforms to run analysis faster than waiting on a request.

HR employees are running sensitive policy documents through a chatbot to get quick summaries.

Staff in operations are building workflow automations using free AI builders they found online.

None of it went through IT.

That’s shadow AI. And the reason it matters isn’t just that data is leaving your environment…though it is. It’s that this behavior is accelerating faster than any governance policy can catch up with.

Eventually, everything these people built becomes your problem to secure, maintain, and explain to auditors.

How to Build AI Workflows Over Your Data

Some things don’t really “click” until you see them in action. You hear people talking about some new technology, trend, or tool. But you don’t really “get it” until you see it.

That’s how data-driven AI workflows were for me.

I’d heard the AI hype. Then I saw an AI assistant working over live business data. My wheels started spinning. It opened up so many possibilities. 

Then I saw that AI assistant added to a workflow. Data was passed to the assistant, which then made a decision…and led to another workflow step, which could then lead to another assistant and so on.

That’s when it really clicked. You can do anything with this.

In this article, I’ll explain more about AI workflows and why they’re so important to businesses. What are they? How do they work? Why should you care?

Also, we’ve included a video that walks through the whole process of creating an AI workflow from start to finish. After all, some things don’t really “click” until you see them in action.