Evaluating Third-Party AI Models: 3 Questions for Technical Feasibility

When clients ask about using NVIDIA's latest models, first ask: 1) Can we control inference costs at scale? 2) Are there clear APIs for logging edge cases? 3) Is the model's training data recent enough for our problem domain? The 'cult of the latest' often overlooks operational constraints. For example, a client recently wanted to deploy Phi-3 for code gen despite having a legacy Python 2 stack. The mismatch cost them 3 weeks in compatibility testing. Always prototype in low-fidelity before full integration. According to MLOps reports, 68% of failed AI projects were due to infrastructure gaps discovered after implementation. Document these checks in discovery calls to prevent expensive roadblocks.