In 2016, I joined 9GAG — a top-150 global site with 150 million users and Y Combinator backing. As part of a small team, we built features and kept the platform running for millions of daily active users. Scaling a high-traction product brings constant, real-time challenges.
Fast-forward to 2026: with mature cloud platforms, highly scalable software is no longer a luxury reserved for giants. Proven tools and managed services now let us ship fast while minimizing infrastructure overhead. The real opportunity lies in redirecting our energy from "fighting solutions" to actually delivering business value.
The conventional path still works: ship quickly, discover bottlenecks, refactor, and adapt. But every firefight consumes team energy and slows momentum. What if we could design for scale from day one — with minimal extra effort and without falling into premature optimization?
It's rarely black-and-white. The art is knowing when to build simple and iterate later, and when to invest just enough upfront structure so that growth feels like acceleration rather than recovery.
Most applications are read heavy, yet writes, especially concurrent updates, remain expensive. They introduce locking, resource contention, and sudden spikes in latency. Connection pools are typically small and easily exhausted. Common pitfalls such as excessive joins, missing indexes, or the classic N+1 query problem can quickly turn the database into a busy single point of failure.
Addressing these challenges has never been simple. Even OpenAI has invested heavily in scaling its database infrastructure: Scaling PostgreSQL. The traditional path usually follows a predictable sequence:
We could keep repeating this playbook every few years — but technology evolves faster than most teams realize.
With these advancements, true sharding, once the default solution for massive scale, has become a last resort for many teams. It brings significant complexity, including re-sharding, cross-shard queries, and consistency challenges. In many cases, functional data separation — organizing systems by domain such as accounts, products, billing, or analytics — delivers most of the benefits with far less operational overhead.
You cannot improve what you do not measure.
Observability should be built into the system from day one — never added as an afterthought. This means setting up analytics, performance insights, and distributed tracing as early as possible.
Vercel Analytics reveals where users bounce and where engagement drops. Speed Insights highlight pages that need optimization and expose potential bottlenecks. OpenTelemetry provides granular visibility into every function's lifecycle.
Today, it has never been easier. Tools like Vercel Analytics, Google PageSpeed Insights, Next.js's built-in OpenTelemetry plugin, and Sentry deliver rich, multi-layered insights with minimal effort. Modern LLM-powered tools are already taking observability to the next level.
Clear metrics create clarity and discipline. Define explicit targets such as:
When these numbers are visible and tracked consistently, performance conversations shift from opinions to evidence. Instead of guessing where the system feels slow, you can pinpoint issues with confidence and improve deliberately.
Design for horizontal scaling from day one — treat it as a hard requirement, not a future enhancement.
Stateless applications are the foundation. Every request is independent, so any server can handle any user. When traffic grows, you simply add more instances. Load balancers, auto-scaling groups, and modern platforms do the rest. If one instance fails, traffic is instantly rerouted — no downtime, no drama.
Edge computing takes this model further. Modern platforms now run your code at the edge, close to your users. This dramatically reduces latency, minimizes cold starts, and makes global scaling feel effortless. Static assets are served from CDNs, while dynamic workloads execute regionally by default.
Adopt an event-driven architecture early. Instead of tightly coupled services, structure your system as Event → Queue → Consumer. This smooths traffic spikes, improves resilience, and protects your core application from sudden overload.
Caching is the final critical layer. Tools like Upstash Redis offer low-latency, globally distributed caching that pairs perfectly with serverless environments. However, caching must be intentional. Poorly managed TTLs or simultaneous cache expirations can trigger "cache miss storms" that overwhelm your database. Layered caching strategies and thoughtful invalidation rules prevent small oversights from becoming large-scale incidents.
Together, stateless design, edge execution, and disciplined caching form the modern foundation for scalable systems.
While the vision of truly AI-native auto-scaling and a fully autonomous zero-downtime intelligence layer is powerful and rapidly approaching, these technologies are still in the rapidly maturing phase rather than fully mature.
As Charity Majors argues in Bring Back Ops Pride, product engineering is typically much simpler than infrastructure engineering — and that's by design, because cognitive bandwidth is the scarcest resource in any engineering org. One of the key functions of operations is to absorb the hardest technical problems so that product teams can focus on moving the business forward. That's also why we work so hard to not have hard operational problems at all: no ops is a better ops. No one can do everything, and as the saying goes — the best code is no code at all, the second best is code someone else writes and maintains for you, and the worst code is the code you have to write and maintain yourself.
The smartest approach in 2026 is pragmatic: start with battle-tested platforms for rapid and reliable scaling, and layer on advanced AI-native capabilities only when your specific use case truly demands it. The gap between vision and reality is closing fast, but for now, smart engineering with mature tools still wins.