I keep seeing startups pitch edge computing like it’s the next container revolution. Investors nod. Architects draw diagrams with little boxes at “the edge.” Nobody asks the obvious question: do you actually have a latency problem that geography can fix?
At Dropbyke we processed GPS telemetry from thousands of bikes across Seoul. Real IoT. Real device data. Real volume. You’d think that’s a textbook edge case. It wasn’t. A well-tuned message queue and a couple of regional cloud instances handled everything we needed. Sub-second was easy without edge infrastructure.
The edge is a spectrum, not a destination
Device. Network edge. Regional edge. Cloud. Each hop adds latency, sure. But each hop closer to the device also adds operational pain: deployment complexity, consistency headaches, debugging in the dark.
The math only works when you have a genuine constraint:
- Latency below 50ms on a critical path. Not aspirational latency. Measured, user-impacting latency.
- Bandwidth costs that actually hurt. If you’re shipping raw video or sensor streams, processing locally makes sense. If you’re sending JSON, it doesn’t.
- Unreliable connectivity. Factory floors, moving vehicles, rural deployments. The network genuinely can’t be trusted.
- Data residency requirements. Regulation says the data stays put. Fine. That’s a real constraint.
If none of those apply, you’re adding distributed systems complexity for a problem you don’t have.
What edge computing actually costs you
Consistency goes out the window. You’re now designing for eventual convergence, conflict resolution, and partial failures at every node. Your deployment pipeline needs to handle hundreds of locations instead of a handful of regions. Observability becomes sampling and aggregation because you can’t ship raw telemetry from every edge node without defeating the purpose.
Security surface area multiplies. Every edge node is a potential compromise point. You’re trusting code running in locations you don’t physically control.
Now at Decloud, we’re building cloud infrastructure tooling. I see teams adopt edge patterns and then spend months building the operational scaffolding that a centralized architecture gives you for free. The edge didn’t solve their problem. It replaced one set of problems with a harder set.
When it’s actually worth it
CDN edge compute is the one place where the tradeoff is almost always favorable. Auth checks, request routing, simple personalization – these are stateless, short-lived operations running on infrastructure someone else manages. Cloudflare Workers and similar products nailed this by keeping the programming model simple and the blast radius small.
Beyond CDN, edge computing earns its complexity in exactly three scenarios: real-time industrial control, high-volume media processing at the source, and latency-sensitive interactive applications where every millisecond is measurable in revenue.
Everything else? Run it in the cloud. Optimize your queries. Use a CDN. Move on.
Bottom line
Edge computing is a targeted optimization, not an architecture paradigm. Treating it as a default is resume-driven development dressed up as forward thinking. Start with the simplest thing that works, measure where it doesn’t, and push compute to the edge only when the numbers force your hand.