To understand microservices, it helps to first understand what they're an alternative to: the "monolith."
A monolithic application is built as a single, unified codebase — accounts, payments, notifications, search, all in one deployable unit. Simple early on, but everything becomes tightly coupled as it grows.
Some of the largest tech platforms in the world run thousands of independent microservices working together, each maintained by a different small team.
With microservices, that same application is broken into separate, independently deployable services, each with its own codebase, communicating through APIs. Each can be updated or scaled independently.
Microservices solve real problems at scale but introduce real complexity: more moving parts to monitor, more network calls, more coordination required. This is why they tend to make sense for larger, more mature products.
NOXEL SEO, Forge, and Nexus already run as separate, independent products connected through clean APIs — the same principle behind microservices, applied at the product level.
Start with a well-organized monolith. Split out a microservice only when you have a clear, specific reason — a part of the system that needs to scale independently.
No. Microservices add real operational complexity. For small products, a well-organized monolith is often faster to build and maintain.
Not necessarily. Many products scale to significant size on a monolith before microservices become genuinely necessary.
Usually through APIs — typically REST or messaging queues — the same core concept as external APIs, applied internally.
A single, unified application codebase where all features are built and deployed together as one unit.
Yes — Netflix is one of the most commonly cited examples of a large-scale microservices architecture, running hundreds of independent services.
A single entry point that routes incoming requests to the correct microservice, often also handling authentication and rate limiting.
It's possible but usually not recommended — the added complexity tends to slow down early-stage teams more than it helps.
Ideally, only that specific feature is affected while the rest of the system keeps running — a key benefit over a monolith failing entirely.
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