How do you decide between an open-source (self-hosted) LLM and a closed-source API model?
This is an architecture and business decision, not a religious one. What shows depth is weighing capability, cost, privacy, control, and operational burden, then committing to a call instead of declaring 'open is always better.'
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
This is an architecture and business decision, not a religious one. What shows depth is weighing capability, cost, privacy, control, and operational burden, then committing to a call instead of declaring 'open is always better.'
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.
