Eighteen months ago, our infrastructure looked like most growing SaaS companies: a primary cloud region, a disaster recovery region, and a CDN for static assets. It worked. Users could access our platform, data was backed up, and our SLA held steady at 99.9%.

But we had a problem. Our user base had grown from primarily US-based to truly global. Users in Singapore, São Paulo, and Sydney were experiencing latencies that degraded their experience. Our response time SLA was being met, but barely—and only because our SLA was too generous.

The Breaking Point

The moment of clarity came during a customer call with a financial services firm in Tokyo. They showed us their monitoring data: our API calls were taking 400-600ms round trip. For their use case—real-time trading decisions—that was unacceptable. We were about to lose a $2M ARR account.

We realized our architecture wasn't just slow—it was fundamentally wrong for a global product. Speed of light isn't a software problem you can optimize away.

That call triggered a six-month infrastructure overhaul. We moved from centralized cloud computing to edge computing, deploying our core services across 42 locations worldwide. Here's what we learned.

What We Actually Changed

Edge computing isn't just about CDNs for static content. We moved actual computation to the edge—API servers, business logic, even portions of our database layer. The architecture change was significant:

• Deployed stateless API servers to edge locations using Cloudflare Workers
• Implemented a distributed caching layer with regional consistency guarantees
• Redesigned our data model to support eventual consistency where appropriate
• Built a smart routing layer that directs requests to the optimal edge node
• Created a centralized control plane that manages configuration across all edges

The Hard Parts

I won't pretend this was easy. Distributed systems are hard. Globally distributed systems are harder. We hit every classic distributed systems problem: split-brain scenarios, clock drift, conflict resolution, and the CAP theorem's uncomfortable tradeoffs.

Data Consistency Challenges

Our biggest challenge was data. Some data absolutely needed strong consistency—financial transactions, for instance. Other data could tolerate eventual consistency—user preferences, cached recommendations. We had to carefully categorize every data type and build appropriate handling for each.

Operational Complexity

Monitoring 42 edge locations is different from monitoring two cloud regions. We rebuilt our observability stack, implemented distributed tracing, and created new alerting rules that account for edge-specific failure modes.

The Results

Six months after completing the migration, our metrics tell the story. Global P95 latency dropped from 580ms to 47ms. Customer satisfaction scores increased 23%. And yes, we kept that Tokyo client—they're now one of our biggest advocates.

• P95 latency: 580ms → 47ms (92% reduction)
• Global uptime: 99.9% → 99.99%
• Customer satisfaction: +23%
• Infrastructure cost: +15% (worth it)

Should You Do This?

Not every company needs edge computing. If your users are concentrated in one region, centralized infrastructure is simpler and cheaper. But if you're building for a global audience—especially one with latency-sensitive use cases—edge computing is worth the investment.

The technology has matured significantly. What would have required a massive engineering team five years ago can now be done with managed platforms. The hard parts are still hard, but they're more manageable than ever.