This question has so many branches that I spent the first few minutes just trying to figure out where to start.
Start by framing the decision as context-dependent, not a binary choice, and anchor it to Lyft's scale and AI workloads. Then systematically walk through each dimension (scaling, deployment, DX, migration, data ownership, observability) with concrete trade-offs, and conclude with a pragmatic recommendation that often blends both approaches.
Pro tip: Emphasize that for AI/ML systems, the real trade-off is often between model serving latency and iteration speed—microservices add network hops that can hurt inference latency, but they also allow independent scaling of GPU-heavy services. Show you understand that 'monolith vs microservices' is a false dichotomy; the right answer is usually a modular monolith evolving into selective microservices.
Ask about team size, expected scale, latency requirements, and deployment frequency to ground the discussion. This shows you avoid dogmatic answers and tailor architecture to the situation.
Systematically address scaling (vertical vs horizontal, independent scaling), deployment (single artifact vs orchestrated services), and developer experience (local setup, debugging, cognitive load).
Discuss how monoliths share a database while microservices demand bounded contexts and eventual consistency. Mention patterns like CQRS, saga, and API composition.
Explain incremental migration strategies (strangler fig, branch by abstraction) and the increased observability burden (distributed tracing, centralized logging, metrics) in microservices.
Conclude with a recommendation that fits Lyft's AI use cases—e.g., start with a modular monolith for rapid iteration, extract high-scale or GPU-bound services as needed, and invest in observability early.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.