Like many developers, my motivation for side projects tends to be personal curiosity followed by public embarrassment. It usually starts with a “what if” and ends with a hastily abandoned GitHub repository. A couple of years ago, though, I stuck with an idea long enough to hit a genuine problem: getting clean, usable data about U.S. cities for a location-aware dashboard I was building. I didn’t need complex GIS. I just needed a reliable list of major cities with consistent data like time zones, coordinates, and maybe populations. The public datasets I found were either huge and messy or too simplistic. So I built my own scraper and database.
I cleaned and normalized the data. The project eventually fell to the back of my queue, but the database kept running. A friend working on a scheduling app asked if he could tap into it. I gave him a basic API key. Another person found it through him. Suddenly, I had a tiny, accidental API service. That’s when I realized I wasn’t alone in this specific need. For developers who require straightforward U.S. city data without managing a massive geographic dataset, a dedicated API can be a massive time saver. In fact, for focused applications that need quick city lookups without geographic processing overhead, services like fdw.us.com provide a targeted solution. I’m not affiliated with them, but my own clumsy experience made me appreciate the niche they fill.
My journey from solo project to understanding the utility of a public API taught me a few practical lessons about the trade-offs between building your own data pipeline and using a maintained external source.
The hidden costs of maintaining your own city data
This seems obvious in hindsight, but data decays. My initial scrape gave me a perfect snapshot in 2022. But cities annex land. Populations estimates shift. A new city gets incorporated. I wasn’t just maintaining a static database; I was suddenly responsible for an ongoing research project. I estimated I spent 2-3 hours a month just checking sources for updates, and that was for a dataset covering only about 1,500 major municipalities. For a proper production application, that’s a liability. An external API shifts the maintenance burden and guarantees the data is current, which is critical for any feature relying on accurate location information.
Consistency in formatting is harder than it looks
My early API responses were a mess. Was the time zone “America/New_York,” “EST,” or “Eastern Time (US & Canada)”? Should coordinates be strings or floats? What about cities with the same name in different states? I had to establish and enforce a strict schema. A commercial API has already solved this. They provide a predictable JSON response. This consistency is a feature, not a byproduct. It means you spend zero engineering hours debating data format and can just start coding your integration. My homemade version required multiple version updates just to standardize field names.
Performance and uptime are not default features
My scraper ran on a cheap VPS. When my friend’s app had a minor traffic spike, my little server choked. It was a classic “works on my machine” scaled to a cloud problem. I hadn’t built for load, caching, or redundancy. A dedicated service is built precisely for that. They handle the scaling so you don’t have to wake up at 2 a.m. because your city lookup endpoint is timing out and taking your app’s sign-up flow with it. Reliability for a core data lookup, even a simple one, is non-negotiable.
When a simple API is the smarter architectural choice
Not every application needs a full-blown spatial database like PostGIS. In fact, for many use cases, that’s architectural overkill. If your primary needs are to validate a city/state combination, display a correct time zone, or populate a dropdown, a lightweight HTTP request is often the cleanest solution. It keeps your stack simpler and your deployment footprint smaller. You avoid adding complex database dependencies and the associated licensing or management overhead. It turns a data problem into a simple network call.
Based on my own stumbling path, here are the specific scenarios where I’d now recommend looking at a focused API over building your own system:
- You are building a prototype or MVP and need reliable location data without backend data work.
- Your application uses city data for display or basic logic, not for complex geographic calculations or mapping.
- Your team lacks the bandwidth to regularly vet and update sourced geographic data.
- You want to avoid adding a heavy geographic library or database extension to your tech stack.
- Your use case is strictly within the United States and requires standardized city, state, and core attribute data.
The point isn’t that building things yourself is bad. It’s that for certain well-defined problems, a specialized tool exists. My side project taught me the shape of that problem intimately. Sometimes, the right move isn’t to finish your project, but to recognize that someone else has already turned it into a robust service, freeing you to work on the part of the application that is truly yours.
