What Does Scalability Mean in App Development?

Sometimes the moment creeps up: the app feels a "bit" slower, support tickets pile up, and release days become tense.

And sometimes it arrives with a bang. A campaign takes off, a press mention brings a peak, or your purpose project is shared in a newsletter. From 500 daily users to 50,000—not over a year, but over a weekend.

In our projects, we see: Scalability rarely becomes important due to a love of technology, but rather a love of trust. When an app falters under load, it’s not just “an error” occurring. Something happens in people's minds: they drop off, ratings drop, teams enter crisis mode.

Expectations are brutally honest. Even with mobile web experiences, it becomes clear how little patience there is: Over 53 percent leave a page if it takes longer than three seconds to load. Marketing Dive (Google study, 2016)

Though apps aren’t exactly websites—the emotional logic is the same: “If it doesn’t work immediately, it’s not reliable.”

Our first fresh perspective: Scalability is also impact assurance. If you're building an educational app that should reach more people, or a platform that mobilizes donations, then stability is not just a technical issue. It’s part of your responsibility: Your mission must not fail at an overwhelmed login endpoint.

That's why at Pola, we start early with a simple but crucial question: What must your app be prepared for—planned growth, unplanned peaks, or both? This distinction later determines whether you primarily prioritize capacity, elasticity, or robustness.

When someone says “We need to make the app scalable,” they often just mean: “More users should be able to access it simultaneously.” That’s important—but it’s only half the story.

Scalability has two growth axes:

First: Load Growth. More simultaneous requests, more data traffic, more devices, more background tasks (push, sync, uploads). The app market continues to grow, as does the expectation that everything just works. The sheer density alone shows the pressure: In 2023, there were over 3.7 million apps in the Google Play Store and about 1.8 million in the Apple App Store. Selleo

Second: Feature Growth. New features, roles, integrations, markets. An app that started with 5 screens becomes a product with rules, special cases, and exceptions over time. And this is where many things tip over: Not because the CPU is too weak, but because every change suddenly becomes risky.

Important is the distinction: Scalability is not the same as performance. Performance describes how fast something is at a certain load. Scalability describes how well the app maintains its performance as the load increases.

An everyday analogy we like to use: Performance is how fast a train travels on a clear track. Scalability is whether the timetable still holds when suddenly three times as many people board—without doors jamming, signals failing, or the entire operation coming to a halt.

Our second fresh perspective: Scalability is team-friendly architecture. In practice, not only does the app grow, but also the team that works on it. When multiple developers need to deliver concurrently, you need structures that decouple changes from each other. Thus, a “scalable app” also means: easily testable, modular, understandable.

When you identify these two axes early, decisions become easier: Some projects need load reserves first, others a clean base for feature growth first. And often it’s a mix—but with a clear emphasis.

When apps collapse “under load,” it often appears externally as a single issue: “Server overloaded.” In reality, it’s almost always a chain of bottlenecks.

The classic is the database. Initially, it’s convenient: a centralized place, all consistent, all traceable. And then comes the moment when a single query suddenly runs a thousand times more often. Or a lock blocks write access. Or a poorly chosen index makes a search a full-text journey.

Equally common are code issues. Not “coded too slowly,” but too tightly coupled. A function calls three others, waits for an external API, and writes logs synchronously on the side. This works with 50 users. At 5,000, it becomes a domino effect.

And then there is the bottleneck that hardly anyone talks about first: processes and releases. If a hotfix can only be applied at night, if deployments cause fear, if nobody knows exactly what to monitor post-release—then it’s not the system that scales, but the stress level.

Our third fresh perspective: Scalability is incident-friendliness. We’re not just building for “more,” but for “when something goes wrong.” That’s a subtle difference: A robust app has clear limits, clear timeouts, clear fallbacks. And it helps the team quickly understand what’s happening.

In practice, we like to adhere to a simple principle that you can immediately adopt: “Keep the critical short.” Everything that is your critical moment (registration, checkout, donation) should have as few dependencies as possible. If you want to send emails, generate PDFs, or update statistics afterward, do it asynchronously.

This also shows why many outages are so costly: Downtime is not just a technical state, but a business damage. Atlassian cites examples where failures in large companies have caused damages in the tens of millions. Atlassian

You don’t have to be Facebook to feel this effect. Smaller products just have less buffer.

When we talk about scaling, we quickly arrive at two basic models: vertical and horizontal.

Vertical means: You give a system more power. More CPU, more RAM, a larger database setup. This is often the first step because it acts quickly and requires little restructuring. But vertical scaling has limits: eventually, it becomes very expensive, and you still have a single point of failure.

Horizontal means: You distribute the load across multiple instances. Not one stronger server, but several—ideally so that you can scale up automatically during peaks and down during quieter times.

For horizontal to work, you usually need two things: a Load Balancer (which distributes traffic) and services that are stateless. It sounds technical, but it’s easy to understand: If a user login only works on Server A because that’s where the session is, Server B can’t help. If the state is instead held in a common store (e.g., in a database or a cache like Redis), any instance can take over.

In practice, scaling is often a mix: a little vertical to get quick relief, and targeted horizontal where it really counts.

What we always keep in mind: Reliability is a sibling of scalability. Once you work horizontally, you often automatically build in redundancy. If one instance fails, others take over. It’s not just “more performance,” but less risk.

And here comes our Pola perspective: We don’t like “constant maximum performance.” It becomes sustainable when your system is elastic. Additional resources only when needed. This saves costs and avoids unnecessary energy consumption—the technical side of an attitude: don’t waste.

If you’re just starting out, the most important decision isn’t “Kubernetes or not,” but: Can your app fundamentally accommodate multiple instances? If you prepare this well, many paths remain open.

The architecture question is often led unnecessarily ideologically: Monolith bad, microservices good. We see it differently. For many products, a well-constructed monolith is just right at the start: faster to implement, easier to test, easier to understand.

The problem is rarely the monolith itself, but a monolith without boundaries. When everything knows everything, every change becomes expensive.

That's why we like to use a second tried-and-tested method: “Cut by responsibility, not by technology.” Specifically, it means: we structure early by functional areas so you can later isolate individual parts without tearing the whole product apart.

A typical path looks like this:

• Start as a modular monolith with clear domains (e.g., accounts, content, payments).
• If a domain grows significantly or gets special requirements, it becomes its own service.
• Only when teams and operations truly benefit will multiple independent services emerge.

Why this order? Because microservices allow you to manage parts independently, but they bring new tasks: network communication, distributed debugging, versioning, observability. It’s worth it when complexity is already there—not to “buy” complexity.

Here, too, fits an important warning from the startup context: There are indications that a large portion of startup failures is related to scaling too early—often organizationally and strategically, but the idea is transferable. LinkedIn Post (Startup Genome statistic, widely shared)

Our stance on this: Plan the door, don’t build the whole house right away. An app can start as an MVP. But it should be built so that you don’t have to start over with every growth step.

If you want to dive deeper into such decisions, we have accompanied similar architectural questions in app projects, especially where later new functions and user groups were added (e.g., Ureka or Aeri). The context is different every time—the principle remains: clarity before size.