Title: Discussion 14 — Cloud, On-Prem, or Hybrid? Make the Call
Course: AIINFRA 100: AI Systems & Development Environments > Section: Week 14 — Cloud, On-Prem & Hybrid: Deployment Models and Cost Awareness

💬 Discussion 14 — Cloud, On-Prem, or Hybrid? Make the Call (20 pts)

Think of an organization you're familiar with (an employer, a business you frequent, or an industry you're curious about) that collects or generates data — customer records, sensor data, video, transactions, etc. In your initial post, answer:

1. Would this organization's AI needs be best served by a fully cloud, fully on-prem, or hybrid approach? Explain your reasoning.
2. Which specific concept from this week — data gravity, latency, data sovereignty, utilization/cost, or the "free"/idle-capacity trap — most strongly supports your answer, and why?
3. Is there a part of the workload that clearly belongs on the other side of your split (e.g., if you said mostly cloud, what would still need to stay local, or vice versa — and consider both the training side and the inference side)?

Post your initial response, then reply to at least two classmates. In your replies, push on their reasoning: would a different concept from the lecture change their answer, or did they weigh a cost factor (egress, idle endpoints, Spot risk) differently than you would?