The other day, while adding a new financial calculation module to hesapciyiz.com, the first thing that came to mind — as always — was building a more “robust”, more “future-proof” architecture. So: an API gateway, a separate microservice, maybe a message queue… Then I stopped. I told myself “Mustafa, this is just one calculation, don’t overdo it.”
That inner voice should be familiar to anyone wrangling their own small projects. For those of us with a technical background, it’s very easy to jump to the most complex, most “best-practice” solution to solve a problem. But where exactly do you draw the overengineering line in small, indie projects? I want to share my own experiences and thoughts.
Symptoms of Overengineering: When to Stop?
I already manage 13 containers on my own VPS — PostgreSQL, Redis, Next.js apps, this blog itself. Sometimes for a new feature I think “let’s add Kafka to this.” But I know that Kafka cluster is just extra work and maintenance for me. That’s one of the first symptoms of overengineering: trying to solve a problem with a more complex tool when it could be solved with what you already have.
In fact, once, while adding a new feature to this blog’s AI generation pipeline, I thought “let’s move this to Kubernetes too, it’ll be more resilient.” Thankfully I stopped right there, because while a 2.5 GB RAM Astro build was already pushing my system, that complexity could swallow me and the server. That kind of “solution” is trying to solve a problem that doesn’t exist. My AI pipeline runs a few times a day; the operational cost of Kubernetes for this use case would be utterly absurd.
Costs and Trade-offs
Overengineering, especially for one-person or small-team projects, has serious costs. Those costs aren’t just money — they’re far more valuable things: time, energy and flexibility.
The Cost of Time
A more complex architecture means more code, more tests, more debugging and more integration. I already have four side products (hesapciyiz.com, spamkalkani.com, islistesi.com, this blog). I can’t dedicate that much time to each. Last month when I wrote sleep 360 and got OOM-killed, even finding that simple bug ate my time. In a more complex system that turns into a nightmare and can sour you on the project entirely.
Operational Cost
Complex infrastructures need more monitoring, more maintenance and more troubleshooting. Scenarios like the Docker disk fire I had on my VPS on April 28th when the disk filled to 100% can give you a headache even on a simple system. A disk filled to 100% because of 33 GB of build cache and 23 GB of unused images is just one of the thousands of issues you’ll meet when you build a more complex system. Carrying that operational load alone holds your project back.
Loss of Flexibility
Early-stage “future-proof” designs can actually make future changes harder. When your business model isn’t settled and you don’t yet know exactly what your users need, big architectural decisions lock you in. A change in feature or direction can turn into a giant refactoring project. That seriously limits your ability to “pivot.”
My Approach: Simplicity and Pragmatism
I have a few core principles I lean on to avoid the overengineering trap in my own projects. They are things I learned through experience, mistakes, and a lot of “good enough” moments.
Minimum Viable Architecture (MVA)
My rule is simple: I build only as much architecture as the current need demands. For example, on hesapciyiz.com I use SQLite instead of PostgreSQL as the database for exactly this reason. Yes, PostgreSQL is more powerful, but SQLite’s operational cost is zero. For me that was a trade-off: I picked operational ease over powerful features. In the early phases of a project, instead of getting stuck on performance or scaling, I focus on shipping the core functionality the simplest way possible.
Need-Driven Development
The question “How much architecture do I need to solve this problem today?” is always sitting in the back of my mind. While building a feature, instead of jumping straight to the most complex solution, I think about how I can integrate it more simply into my existing system. For example, this blog runs on Astro, Node, SQLite and Nginx. Why this combination? Because it gives me simplicity, control and enough performance. Rather than adding unnecessary complexity, I lean on tools I know and can manage easily.
Incremental Refactoring
If I need to refactor later, I’ll refactor later. My AI-driven content pipeline started as a very simple cron job. That basic structure did the job at first. Later, as needs grew, it evolved into a sturdier shape with GitHub Actions and Cloudflare Workers. The process moved as need arose; I didn’t try to think of everything up front. Even running my own self-hosted runner on my VPS, just to avoid blowing through the GitHub Actions quota, gave me both cost and control advantages — that came out of my self-hosted runner economics experiences.
A Decision-Making Checklist
When deciding on a new feature or architecture, I have a few questions I ask myself. They help guard me against overengineering:
- Does this feature actually solve my current problem? Or is it just in the “would be nice” category?
- How much will this extra complexity raise my operational load? How well does it fit my existing system?
- Is there a simpler alternative? Maybe less “elegant” but more practical?
- How urgent and certain is the future scaling need? Will I really reach millions of users one day, or is that just an assumption?
- What’s the return on this investment? Will it be worth the extra time and effort?
Conclusion
Fighting overengineering in small projects is an ongoing process for me. Sometimes I catch myself thinking about a distributed cache or a complex event bus again. But then I pull the brake and tell myself “take a breath, think of the simplest path.” I made — and still make — mistakes on this journey. I felt the pain of having to clear directories under _work/_temp because of GitHub Actions runner state corruption; I wrestled with AI quirks like a slash in a tag or a quoted string in publishDate in my AI pipeline. What matters is taking lessons from those mistakes and stepping more deliberately into the next project.
So where do you draw the overengineering line in your own small projects? Or have you had a similar “disk fire” experience like mine? I’d love to hear it in the comments.