Self-host vs Managed-cloud Decision — A Project-Kickoff Rubric

Overlay, not a deep dive. This skill answers where to run (self-host vs managed), not which engine ([[agentsop-llm-engine-selection]]) or how to build the app ([[agentsop-dify]]). It fires first, at kickoff, and hands off to those once the side is chosen.

1. 何时激活 (When to Activate)

1.1 直接信号 (Direct triggers)

• At kickoff you must decide where an LLM or LLM platform runs: a managed API/cloud (OpenAI / Anthropic / Bedrock / Dify Cloud) vs your own GPUs / your own Docker (vLLM, self-hosted Dify).
• Cost pressure: monthly managed spend is climbing; someone says "should we just run our own and stop paying per token?"
• Compliance pressure: a data-residency / air-gap / regulated-data requirement (finance, medical, gov, GDPR region-lock) appears and the managed path is suddenly in question.
• You're comparing a self-hostable platform's tiers — e.g. Dify Cloud Pro ($59) vs self-deployed Docker [architjn.com/blog/dify-cloud-pricing-plans], or managed-vLLM-as-a-service vs your own H100s.

1.2 反向信号 (Skip this rubric when)

• The decision is already self-host, and the open question is which engine → go to [[agentsop-llm-engine-selection]] (vLLM vs TGI vs SGLang vs TensorRT-LLM vs llama.cpp).
• The decision is already self-host Dify, and the open question is how to build/operate it → go to [[agentsop-dify]].
• Single user / hobby / one stream — the answer is "just call the managed API"; no rubric needed.
• Training / fine-tuning siting — different cost structure (burst GPU, spot, not steady-state serving).