Microservices Architecture Plan Template

Start with a modular monolith and extract services only when you have a clear scaling or team autonomy need. Microservices add network complexity, operational overhead, and debugging difficulty. If your team has fewer than 15 engineers, a well-structured monolith with clear module boundaries is usually a better choice. Extract a service when a module needs to scale independently, deploy on a different cadence, or be owned by a separate team with minimal coordination.

Align service boundaries with business capabilities, not technical layers or database tables. Business capabilities change less frequently than technology choices. Use domain-driven design (DDD) bounded contexts to identify natural boundaries. A good heuristic: if two engineers working on different services need to coordinate their changes frequently, the boundary is probably in the wrong place.

Use the saga pattern. In a choreography saga, each service emits events that trigger the next step, and each step has a compensating action for rollback. In an orchestration saga, a central coordinator manages the workflow. Choreography is simpler for 2-3 step workflows. Orchestration is clearer for complex workflows with branching logic. Never use distributed transactions (two-phase commit) in production microservices. They create tight coupling and single points of failure.

There is no universal answer. A common rule of thumb is one service per team of 4-8 engineers. If you have 20 engineers, aim for 3-5 services, not 20. Too many services create operational overhead that slows down delivery. Start with fewer, larger services and split them when the team or scaling needs justify it. The number should grow organically, not be designed upfront.

Invest in three observability pillars before going to production. Distributed tracing (every request gets a trace ID that flows across all services), structured logging (JSON logs with trace IDs, service names, and timestamps aggregated in a central tool), and service-level metrics (request rate, error rate, latency per service). Together these let you follow a request from the edge to the database and identify where it failed or slowed down. ---