These days, using a tool such as Cursor, GitHub Copilot, Zed, or Claude makes it easier than ever to develop and deploy applications. You express your requirements, receive the completed project back as output, and there you have it! You now have an application that is in production and functioning.

However, the surprise comes after the app has been deployed.

When your app breaks or behaves abnormally, it may not be immediately obvious what is wrong or how to fix it. AI-based development tools can help with debugging, but they will often result in a frustrating experience of going in circles while trying to fix an issue or recommending a solution that doesn’t actually solve the problem. And that’s when you will notice:

• Your cloud costs are too high
• Significant downtime you didn’t expect
• Your users are experiencing slow app performance

That is why it is critical to monitor your application in order to see what is really happening with your application. AppSignal’s tools are a perfect fit, including the new MCP server, to integrate into your development workflow and to quickly identify problems with your application.

The Vibe Coding Blind Spot

AI-generated code usually does what you asked it to do. That’s the part that makes it so powerful.

But it often skips over the details that matter most in production. For instance, the logical structure of the code executes and passes all tests, but the query retrieves 10 times more records than necessary. The error handler catches exceptions without doing anything, and the cache grows without limits.

These aren’t obvious failures, as your app still runs. But over time, they lead to slower performance, higher costs, and harder-to-debug issues.

Monitoring fills that gap and helps you see what the code is actually doing and not what it looks like it’s doing.

Metric #1: Error Rate and Exception Tracking

With vibe-coded apps you need to be extra careful and make sure errors aren’t arising and throwing off your users.

Vibe-coded apps often have inconsistent error handling, making production errors surface only when users hit something broken enough to complain.

This is what you need to monitor:

• Unhandled exceptions that crash your Node.js process (or whatever stack you're running)
• Swallowed errors (like try-catch) that catch everything and do nothing with it
• Rejected promises, because that's something the generated code often just... forgets

If you don’t track these, something critical might stop working by the time you realize it, and you’re left trying to fix it without any context.

AppSignal groups these errors by frequency and common patterns, so you can see at a glance which ones are recurring. The same error appearing 500 times over 5 hours is a code path that needs attention, even when the app is running technically fine. This helps you answer questions like: is it happening on a single endpoint or all? Did this error appear after the last deployment?

Metric #2: Response Times and Slow Endpoints

AI-generated code tends to be suboptimal for latency. The endpoint returns the right data, just sometimes in 5 seconds instead of 200ms.

Don’t rely only on averages. Look at how your slower requests are performing, too. For instance, an endpoint might feel fast most of the time, but occasionally take a few seconds to respond. That’s the kind of issue averages tend to hide, and you won’t even notice those on your dashboard until a user sends you feedback.

AppSignal breaks response times into three measurements: how fast your typical request is, how fast your slower requests are, and how fast your slowest ones are. If that slowest range keeps increasing over time, something in the app is struggling under certain conditions. When you next spot the pattern, your next step should be figuring out where the time is going, whether a database call or a third-party API is sitting in between. AppSignal’s request breakdown shows you exactly which layer is responsible.

Start tracking from day one. After a few days, you have a baseline, and when something changes, it shows up immediately, rather than weeks later when users start complaining.

Metric #3: Database Query Performance

Most of the time spent debugging within a vibe-coded application occurs at the database level. This is not due to mistakes made in developing the logic behind coding and creating applications, but rather because AI-generated queries are created for correctness without consideration for how the database will change (how many rows will be returned), and the indexing requirements for effective querying.

Several classic problems come up repeatedly:

• N + 1 Queries
• Missing indexes on columns
• Queries returning entire data when only specific fields are needed

Therefore, unless you are paying attention to the logs of the database while performing requests to view/create data in an application through vibe-coded applications, you may not notice these problems until a good amount of time has passed.

AppSignal provides you with a detailed breakdown of how each endpoint is generating multiple queries when requesting that endpoint.

Metric #4: Memory and CPU Usage

Nobody really thinks about resource efficiency when they’re vibe coding. People just want working software, not necessarily efficient software. So you end up with the code that runs, but maybe not perfectly optimized for performance.

AI-generated code has this tendency to hold onto memory well past its expiration date. A cache that never stops growing. An event listener that should have been removed but wasn’t. Some blob of data that should have been cleaned up three requests ago but is still just… sitting there. None of this kills your app instantly. But give it time, memory usage keeps climbing. Eventually, your costs go up, your app starts dragging, or your process slows down and restarts without so much as a warning.

CPU spikes tell a similar story. Watch for stuff that should run async but doesn’t. AI code loves synchronous operations—they’re easier to write. But when those operations block the event loop, that’s where trouble starts. Everything was fine in development, and then you get slammed with traffic, and suddenly the whole thing falls over.

When you’re looking at host metrics, the question you’re really asking is: Is my app staying stable between deploys? Memory holding flat? Good. Is memory climbing 50 MB every day? That’s a leak. CPU spiking on every request? Something computationally expensive is running somewhere it really shouldn't be.

AppSignal's Host Monitoring tracks all this along with your application metrics.

Metric #5: Uptime and External Dependency Health

Most vibe-coded apps connect to at least a few outside services. Auth service providers (Clerk/NextAuth), payment APIs (Stripe/Razorpay), AI APIs (Gemini, OpenAI), and more. AI is genuinely good at wiring these up, but the tricky part comes later, when one or more of those services start struggling (maybe due to version issues, missing packages, or something else).

When a dependency starts struggling, the failure is rarely obvious. The app might still technically work, but requests begin taking longer than usual, or an API starts returning occasional errors. From the outside, it just feels like something is off.

If you’re not monitoring this properly, you can spend a lot of time debugging your own code before realizing the issue isn’t yours.

That’s why you need to track:

• External request latency: Are third-party services slowing down?
• Failure rates: Are requests failing more often than usual?
• Uptime monitoring: Is your application actually reachable from outside your system?

This is where AppSignal's Uptime Monitoring functionality comes in. AppSignal has this function available on the free tier of its service. Most other uptime monitoring services, including Datadog, Sentry, and Scout APM, require either a higher tier than a free one or a more complicated configuration to get access to their Uptime Monitoring functionality.

You can check what’s available across plans on the AppSignal alternatives page.

Using AppSignal Inside Your AI Editor (MCP Server)

AppSignal’s MCP server gives AI editors like Zed, Cursor, Claude, and VS Code direct access to your app’s monitoring data. So instead of switching tabs or digging through dashboards, you can check errors, logs, performance, and issues right from your coding environment.

The best part, setup is now simpler than before. Previously, you needed Docker and infrastructure setup, but now you can connect directly to the live MCP server hosted at this endpoint. And once connected, you can start asking things like “show me all errors along with logs in the last 24 hours” and get answers right in your editor. Read more about connecting your AI agent/editor to your monitoring data.

Monitoring as Understanding

Speed is everything. Especially when you want to get your work into the hands of the users. But once you have been launched, this is where you will learn.

Having access to error rates, response times, number of queries, and memory usage, all in one place, makes understanding what is physically happening within your application much simpler. Additionally, it will help you identify bottlenecks and which areas of the codebase need attention.

If you're new to monitoring, don’t get overwhelmed:

• Start with error tracking and response time, as it provides the fastest feedback.
• Implement query monitoring after you have sufficient traffic coming into your application.
• Implement host metrics after you have implemented the above.

This isn't about moving away from developing using AI; it is about the ability for you to track the application’s actual use after it has been designed. Monitoring enables you to understand the use of your application after it has gone into production.