This number determines everything else. A chatbot can wait two seconds. Self-driving systems need milliseconds. Your answer dictates edge versus cloud, model size limits, whether your cost structure even pencils out. Get this wrong and you're rebuilding the entire stack.

When production breaks at 2am, what happens? Simple rollback sounds perfect until you've deleted a database column. Some teams burn weeks enabling rollbacks that never work cleanly. Others fix forward. Know which your architecture supports before you're debugging in the dark.

Code works in development, fails in production. Not a mystery. Environmental drift. The worst debugging sessions trace back to subtle differences nobody documented. If CI, staging, and production aren't genuinely similar, you're guessing whether deployments will work.

Every component inherits its vendor's health. Can you hire people who know this technology? Will it get security patches? Components become liabilities when vendors fade. Evaluate the ecosystem and community momentum, not just today's feature list.

Monday's impressive demo becomes Friday's emergency. Missing GPU drivers. Unhandled edge cases. Dependencies that work on one laptop. The gap between "works in the demo" and "runs reliably at scale" tells you whether you're weeks away or months.

ML teams excel at models, not production infrastructure. Before choosing deployment approaches, assess actual engineering capacity. The technically superior solution your team can't maintain becomes the wrong solution. Match architecture to team capabilities, not aspirations.