Let us start with the uncomfortable truth: most applications do not need microservices. If your team has fewer than 20 engineers and your monolith is still manageable, the operational overhead of a distributed system will slow you down, not speed you up. That said, there comes a point where deploy conflicts, scaling bottlenecks, and team coordination costs make the migration worthwhile. This guide is for teams at that inflection point.
The strangler fig pattern remains the most reliable approach to incremental migration. Rather than rewriting your monolith from scratch — a strategy that has killed more projects than we can count — you gradually extract functionality into independent services while keeping the monolith running. Start with the domain that has the clearest bounded context and the highest scaling pressure. For one of our clients, this was the notification system: it had clear inputs and outputs, was causing database contention, and needed to scale independently during peak hours.
Data is where migrations get messy. Each new service needs its own database, but your monolith's schema has years of implicit relationships and shared tables. We use an event-driven approach with a shared message bus (typically RabbitMQ or Amazon SQS) to keep services loosely coupled while maintaining data consistency. Each service publishes domain events when its state changes, and other services subscribe to the events they care about. This eliminates direct database dependencies and makes the system more resilient to partial failures.
Observability is non-negotiable in a distributed system. Before extracting your first service, invest in distributed tracing (we recommend OpenTelemetry), centralized logging, and health dashboards. You need to be able to trace a single user request as it flows through multiple services, identify bottlenecks, and detect failures before your users do. The monitoring infrastructure you build during migration will pay dividends for years — it transforms debugging from guesswork into science.