agentsop-selfhost-decision
Project-kickoff rubric for the self-host vs managed-cloud decision — when is running your own inference engine / LLM platform worth the ops cost vs paying per-token for a managed API? Decide on two axes — VOLUME (a cost-crossover slider) and COMPLIANCE (a hard gate). Use at kickoff when choosing where to run inference, or when cost / data-residency pressure forces a re-evaluation.
npx skills add agentsope/SkillAlchemy --skill agentsop-selfhost-decision --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
It provides a project-kickoff rubric to decide between self-hosting an LLM platform vs using a managed API, based on two axes: VOLUME (a cost-crossover slider) and COMPLIANCE (a hard gate). It is activated at kickoff to determine where inference should run, or when data-residency pressure prompts reevaluation.
How it works
- It instructs to first apply a COMPLIANCE GATE: verify whether data residency, air-gap, or regulated data requirements forbid leaving your boundary. If yes and no compliant in-region tier exists, self-host; if compliant managed tier exists, allow managed.
- If compliance permits, compute VOLUME crossover: estimate monthly volume, calculate managed_cost = V × $/token and selfhost_cost = (GPU + OPS_LABOR + infra) + V × marginal. Derive V* = fixed / (managed_$tok − marginal_$tok). Below V*, managed wins; above V*, self-host may win if other conditions hold.
- Check HONEST OPS-CAPACITY: confirm there is a DevOps/SRE owner and external components (PostgreSQL/Redis/Vector DB, K8s/Helm) required for self-hosting. If not, managed wins.
- Perform THROUGHPUT-HEADROOM CHECK: calculate replicas = peak_QPS / per-replica_ceiling and re-run the crossover with updated GPU term; if still favorable, proceed to self-host; otherwise reconsider.
- Consider HYBRID options for steady baseline self-hosting with burst traffic to managed API.
- Include PLAN THE FALLBACK steps to flip direction if needed and keep a thin API surface to ease reversibility.
When to use it
- At project kickoff when deciding where an LLM platform runs (managed API/cloud vs self-hosted GPU).
- When monthly API spend climbs and a flip to self-host is contemplated.
- When data-residency or compliance pressure appears mid-project.
- When evaluating self-hostable platform tiers vs own deployment.
- When designing a hybrid topology for steady state plus burst routing.
What it can touch
- The workflow references specific steps and calculations (OP-1 through OP-7) and mentions concepts like GPU term, ops_labor, and replica ceilings. It uses terms like Dify Cloud Pro ($59) and mentions platforms like vLLM and Dify. It does not specify executable scripts or commands beyond described actions.
Caveats
- It requires explicit estimates for tokens per request, monthly volume, and the ops-labor cost; these are not provided and must be supplied by the user. It emphasizes that compliance gating overrides cost and that real-world constraints (ops capacity, throughput ceilings) can invalidate pure cost calculations. It notes potential caveats around replica scaling and hybrid routing, and the need for a reversible flip plan. License/usage implications are discussed in the context of license checks for self-hosted vs managed offerings.
# 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 ques
- 1. 何时激活 (When to Activate)
- 1.1 直接信号 (Direct triggers)
- 1.2 反向信号 (Skip this rubric when)
- 1.3 心智门槛 (Mental check)
- 2. 核心心智模型 (Core Mental Model)
- 2.1 The trade, in one line
- 2.2 Two axes, not one — and they are not symmetric
- 2.3 The crossover formula (OP-1)
- 2.4 The two side-conditions that invalidate "above V → self-host"
- 2.5 The throughput feedback loop (OP-4)
- 2.6 Reversibility is a design choice (OP-7)
- 3. SOP 工作流 (Standard Operating Procedure)
- 4. 操作模型 (Operation Model: Trigger / Action / Output / Evidence)
- OP-1: Volume-crossover calculation
What does the agentsop-selfhost-decision skill do?
Project-kickoff rubric for the self-host vs managed-cloud decision — when is running your own inference engine / LLM platform worth the ops cost vs paying per-token for a managed API? Decide on two axes — VOLUME (a cost-crossover slider) and COMPLIANCE (a hard gate). Use at kickoff when choosing where to run inference, or when cost / data-residency pressure forces a re-evaluation.
How do I install it?
Run `npx skills add agentsope/SkillAlchemy --skill agentsop-selfhost-decision --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From agentsope/SkillAlchemy, a repository with 255 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.
