What tradeoffs would you consider when choosing between a synchronous framework (e.g., Flask/Django) and an asynchronous one (e.g., FastAPI) for a new service?

The choice is not about which framework is faster; it is about which concurrency model fits the workload and the team. Synchronous frameworks such as Flask and Django are simple to reason about: each request occupies a thread from a pool, blocking calls are fine, and the ecosystem is mature with battle-tested libraries for auth, ORM, admin, and background jobs. They scale well for moderate concurrency and CPU-bound or database-bound workloads where the work per request is short. Asynchronous frameworks such as FastAPI and Starlette excel when a single service must hold many concurrent waits, such as fan-out to many downstream APIs or long-lived connections. They achieve high concurrency with low memory per connection, but they require async-compatible libraries end to end. A single blocking call, such as a synchronous database driver or a CPU-heavy function, stalls the entire event loop. The trade-offs are: async gives higher IO concurrency but adds complexity around blocking, debugging, and library compatibility; sync gives simplicity and ecosystem breadth but needs more threads or processes to reach the same concurrency. A pragmatic answer is to choose based on the workload profile, the team's familiarity, and whether the critical libraries have async support.