How Software Architecture Changes As A Product Grows

The C++ Editor That Taught Me About Architecture

The first version of my video editor was written in C++. I chose it for performance. Video editing is heavy work—decoding frames, applying effects, keeping audio in sync—and C++ gives you the kind of control that interpreted languages don’t. I wanted to build something fast, something that could handle 4K footage without breaking a sweat.

What I didn’t anticipate was how much of my time would go into the architecture instead of the features. Every component had to be built from scratch. The UI toolkit, the event system, the memory management, the rendering pipeline. I’d spend a week getting a single panel to display properly, and at the end of it I’d have something that worked but wasn’t particularly good. The features I actually cared about—the timeline, the trimming, the multi-track editing—kept getting pushed back because I was still building the scaffolding to hold them.

After months of this, I hit a wall. The architecture was clean. The code was well-organized. But it was taking forever to make any visible progress. I’d built a beautiful foundation and had almost nothing to show for it. That’s when I realized the problem wasn’t my coding ability. It was the architecture itself. C++ is a powerful language, but it demands a lot of ceremony before you can do simple things. For a solo developer trying to iterate quickly, that ceremony was killing me.

Switching to Electron Was an Architectural Decision

For TrailStudio, I made a different choice. I moved to Electron and web technologies. Electron is often criticized for being heavy and memory-hungry, and those criticisms are fair. But what it gave me was something more valuable than raw performance: the ability to build and change things quickly. The UI layer, which had taken weeks in C++, now took days. PIXI.js handled the timeline rendering with WebGL. FFmpeg did the video processing as a separate process. The architecture wasn’t a single monolithic block of C++ anymore—it was a collection of smaller pieces that each did one job and could be developed independently.

This was the key shift. In the C++ version, everything was tightly coupled. Changing the UI meant recompiling the core. Adding a feature meant threading it through three layers of custom infrastructure. In the Electron version, the pieces were separated. The UI, the timeline rendering, the video processing—they communicated through defined interfaces, not through direct manipulation of each other’s memory. That made the product easier to think about, easier to test, and much easier to extend.

The decision to move to Electron wasn’t about the language itself. It was about choosing an architecture that fit the stage of development I was in. Early on, speed of iteration matters more than raw performance. Later, when the product is mature and users are hitting real performance limits, the trade-offs might look different. But at the start, the architecture that lets you ship features and learn from feedback is the right one, even if it’s not the one you’d choose for a million users.

The Monolith Isn’t the Enemy

There’s a pattern here that applies well beyond my video editor. Most software starts as a monolith. Not because monoliths are elegant, but because they’re simple. When you’re one person or a small team, a single codebase with a single deployment is the fastest way to get something in front of users. You don’t need to manage inter-service communication, handle distributed transactions, or debug issues across network boundaries. You just write code, run it, and see what happens.

The monolith gets a bad reputation because people remember the pain of a large, tangled codebase that’s impossible to change. But the monolith didn’t start that way. It started as a sensible choice for a small product, and it became painful only when the product grew beyond what the architecture could comfortably support. The mistake isn’t choosing a monolith at the start. The mistake is not recognizing when the monolith has stopped serving you.

For a solo developer building an early-stage product, a distributed system is often the wrong answer. Microservices, event-driven architecture, serverless functions—these all add complexity that you don’t need when you’re still trying to figure out what the product is. Each service has to be deployed, monitored, and debugged independently. Each network call is a potential failure point. For a team of one, that’s a recipe for burnout, not scalability.

When the Monolith Starts to Crack

The cracks appear gradually. A monolith works fine when all the code fits in your head. You can make changes quickly because you understand how everything connects. But as the product grows, two things happen. The codebase gets bigger, and the team gets bigger. Either one can break the mental model that made the monolith manageable.

The first sign is usually build and deploy times. What used to take seconds now takes minutes. Then it’s the coordination overhead. Two developers make changes in different parts of the codebase, and their changes conflict. The test suite, once fast enough to run on every commit, now takes so long that people skip it. The architecture that was once invisible becomes something you fight against every day.

This is the point where teams start talking about splitting the monolith. It’s also the point where many teams make the mistake of going too far. They read about microservices and assume that every part of the system should be its own service. They end up with dozens of small services, each with its own database, and a distributed system that’s harder to operate than the monolith ever was. The goal isn’t to eliminate the monolith. It’s to find the boundaries that actually matter—the places where splitting the system makes it easier to reason about, not harder.

The Real Shift Is About Boundaries

In my video editor, the boundary was clear: the UI, the timeline rendering, and the video processing were naturally separate concerns. They had different performance requirements, different development cycles, and different failure modes. Splitting them into separate processes made the architecture cleaner without adding much complexity. FFmpeg is a standalone binary that I call from Electron. PIXI.js is a library that runs in the renderer. The main process coordinates them. It’s not a microservices architecture—it’s a desktop app—but the principle is the same: find the seams in your product and separate along those seams.

For a web product, the seams might be different. A user authentication service is a common candidate for splitting off because it has distinct security requirements and a different change cadence. A billing service is another. A reporting or analytics pipeline that does heavy data processing might benefit from being an asynchronous, event-driven component rather than part of the synchronous request path. These splits make sense because they reflect real differences in how the parts of the system behave, not because a blog post said microservices are the future.

Cloud computing has made this kind of architectural evolution easier. You can start with a single server running a monolith, then gradually move pieces off it as they outgrow it. A serverless function for image processing, a managed database that scales independently, a queue for background jobs. The cloud gives you building blocks that you can compose as the need arises. But the cloud also makes it easy to over-engineer. Just because you can spin up a Kubernetes cluster doesn’t mean you should.

What I’d Do Differently

Looking back at my C++ editor, the mistake wasn’t choosing C++. It was choosing an architecture that maximized future performance at the expense of present velocity. I was optimizing for a scale I hadn’t reached yet, and in doing so I made it harder to reach any scale at all. The product never got off the ground because I was too busy building the infrastructure it would need if it did.

With TrailStudio, I’ve tried to be more honest about where I am. I’m a solo developer building an early-stage product. My bottleneck isn’t CPU cycles or memory bandwidth—it’s my own time and attention. The architecture that serves me best is the one that lets me move fastest, learn fastest, and change direction when I need to. That means making trade-offs that might seem short-sighted to an enterprise architect. It means accepting that some parts of the system aren’t as efficient as they could be, because the efficiency would come at the cost of development speed.

The architecture will change as the product grows. I know that. When TrailStudio has thousands of users and a team of developers, the Electron-based architecture might need to evolve. The video processing might need to move to a server. The timeline might need a more sophisticated rendering strategy. The data model might need to be split into services. But those changes will be driven by real needs, not by a hypothetical future. And when they happen, I’ll be better equipped to make them because I’ll have a product that’s actually worth scaling.

Architecture Follows the Product

The mistake so many teams make is thinking of architecture as something you decide once and then live with forever. But architecture isn’t a fixed artifact. It’s a living thing that evolves as the product and the team evolve. The monolith that works for a team of three won’t work for a team of thirty. The distributed system that a large company needs would be absurd for a startup to build on day one. The right architecture is the one that fits where you are right now, with an eye toward where you’re going.

What that means in practice is that you shouldn’t be afraid to change your architecture when the current one stops working. That change might be as small as extracting a single module, or as large as moving from a monolith to a set of services. The key is to make the change when you need it, not before, and to make it in increments rather than as a big-bang rewrite. Architecture is a series of small decisions made over time, each one responding to the pressures of the moment. Get those decisions right often enough, and the architecture takes care of itself.