When building distributed systems, the "Database per Service" rule is often seen as a strict rule. The common instinct is to create a separate physical database cluster for each microservice to ensure full isolation. However, as your system expands, managing dozens or hundreds of independent database servers can quickly become an operational nightmare.

Early artificial intelligence research, which started in the early 1950s, split into two distinct architectural paradigms. The first was a logic-inspired approach that attempted to hard-code intelligence using symbolic expressions and predefined rules. The second, biologically inspired approach posited that intelligence is fundamentally rooted in learning through networks of simulated brain cells. Rather than writing explicit logic, this architecture focused on enabling a system to learn by recognizing patterns and making analogies. It was inspired by research into how our brain works, realizing that biological networks are highly effective at finding analogies and patterns, and then using them to recreate or recognize information.

When setting up CI/CD for test or staging environments, we immediately want to reach for managed Kubernetes clusters like EKS or GKE. However, for small teams of 1-5 developers and tight budgets, it may not be the best way. A dedicated DevOps specialist and a $70-$100 monthly overhead just for the control plane, on top of main resource costs, sounds a bit extra.