The Architectural Pendulum
For the first two decades of web development, the monolith reigned supreme. A single codebase, a single deployment artifact, and a single massive relational database. As applications scaled to millions of users, this architecture buckled under its own weight. Teams stepped on each others toes, deployments took hours, and a memory leak in the reporting module could take down the entire checkout flow.
In response, the industry swung the pendulum violently in the opposite direction: Microservices.
The Promise of Microservices
Microservices promised independence. The billing team could write their service in Go, the frontend team could use Node.js, and the data team could use Python. Services communicated over lightweight HTTP or gRPC boundaries. If the reporting service went down, the checkout service remained unaffected.
For massive organizations like Netflix and Amazon, this architecture was a necessity. It allowed them to scale their engineering teams across thousands of developers without grinding to a halt.
The Distributed Monolith
However, for the average mid-sized company, microservices introduced a hidden, catastrophic cost: Operational Complexity. What used to be a simple function call in a monolithic codebase was now a network request that could fail, timeout, or drop packets.
Developers suddenly had to handle distributed tracing, eventual consistency across multiple databases, circuit breakers, and complex orchestration platforms like Kubernetes just to get their code to run. Instead of a monolith, many teams accidentally built a “distributed monolith”—a system where services were so tightly coupled that they still had to be deployed together, but with all the added latency of network calls.
The Return to Pragmatism
Today, we are seeing a shift back to pragmatism. Concepts like the “Modular Monolith” are gaining traction. This architecture enforces strict boundaries between modules within a single codebase, offering the organizational benefits of microservices without the operational nightmare of maintaining a distributed system.
The lesson is clear: Do not adopt the architecture of a trillion-dollar tech giant unless you have their scaling problems, and their budget for platform engineering.
