Local, cloud LLMs split workloads

The hot take is that “local vs cloud” is a false binary once you move past solo demos. Most real systems will keep a local path for development and sensitive work, then hand off production-grade traffic to cloud infrastructure.

• –Local setups shine when you want low latency, privacy, cheap experimentation, and no dependency on external APIs.
• –Cloud still dominates for spiky demand, concurrent users, uptime guarantees, and production agents that need predictable throughput.
• –Tool calling is the failure point that turns many “local AI” demos into little more than a chat UI.
• –Hybrid routing is the practical end state: local for quick iteration and private tasks, cloud for scale and reliability.
• –The post is less about model quality and more about operational reality: deployment constraints matter more than ideology.