---
type: concept
title: "Local vs. Cloud Decision Framework"
description: "A four-question decision tree (POC or production? sensitive data? frontier-model capability needed? scaling past ~1,000 users?) that decides whether an AI stack should be self-hosted or run on managed services, instead of treating local-vs-cloud as a dogmatic all-or-nothing rule."
tags: [local-vs-cloud, ai-stack]
videos: [local-vs-cloud-llmsrag-lets-finally-end-this-debate]
created: 2026-07-21
updated: 2026-07-21
---

# Local vs. Cloud Decision Framework

This is the antidote to a debate that is usually argued as an identity rather than an engineering choice. The framing to reject first: "a lot of what I've seen on YouTube is people saying that you have to go local with your AI solution or it is completely bust" [0:00:16]. The honest answer is conditional - "there is a lot of nuance to this and that is what I need to dive into today" [0:00:32] - and the nuance resolves into four questions asked in order.

**1. Is this a proof of concept or production?** For a POC, use managed services. Speed of iteration is the only thing that matters before the idea is validated, and standing up GPUs to test an architecture spends days on infrastructure that may get thrown away. Self-hosting is a decision you make about something that already works.

**2. Is the data genuinely sensitive?** This is the question that can override every other answer, because no amount of convenience compensates for a compliance breach. It is also the question most often answered too aggressively, since the [privacy tiers](./llm-data-privacy-tiers.md) offer a middle rung, and mock data lets a POC proceed without ever exposing the real records.

**3. Do you need frontier capability?** If the workload demands the reasoning, long context, or reliable structured output that only top closed models deliver today, local is off the table until open weights close the gap. If the workload is classification, extraction, routine tool calls, or chat, a mid-tier open model handles it.

**4. Are you scaling past roughly a thousand users?** Below that, per-token API pricing is almost always cheaper than owning hardware. Above it, fixed-cost inference starts winning, and the [cost crossover](./self-hosting-cost-crossover.md) becomes the deciding term.

The answers rarely point one way for the whole system, and that is the point: the framework is applied per component, not per company. A common landing spot is hybrid - sensitive extraction on a local model, hard reasoning on a cloud API, database and orchestration self-hosted. "Local" also does not mean "in your basement", since a cloud box you administer still counts.

## Builds on

- [Local & Self-Hosted AI](./local-ai.md) - the option this framework decides for or against, and the source of the control-not-location definition.
- [AI Tech Stack](./ai-tech-stack.md) - the per-component inventory the four questions are applied to.

## Contrasts with

- [Buy vs. Build](./buy-vs-build.md) - the adjacent decision. Buy-vs-build asks who writes the software; local-vs-cloud asks who runs the hardware, and the answers are independent.

## Related

- [LLM Data Privacy Tiers](./llm-data-privacy-tiers.md) - the detailed answer to question two, including the hosted-open-model middle rung.
- [Self-Hosting Cost Crossover](./self-hosting-cost-crossover.md) - the arithmetic behind question four.
- [Local LLM Hardware Requirements](./local-llm-hardware-requirements.md) - what question three costs in VRAM if you answer it locally.
- [Cloud GPU Hosting](./cloud-gpu-hosting.md) - the hybrid answer, where you rent the machine but still control the stack.
- [Local LLMs as Agents](./local-llms-as-agents.md) - the capability ceiling that most often forces a cloud answer to question three.
- [Model Selection](./model-selection.md), [Multi-Model Agents](./multi-model-agent.md) - the per-step version of the same choice inside one system.
- [Provider Independence](./provider-independence.md) - why the answer should stay reversible whichever way it lands.
- [Agent Deployment](./agent-deployment.md), [Containerization & Docker Networking](./containerization.md) - how a self-hosted answer actually ships.

## Tools

- [Ollama](../entities/tools/ollama.md) - the local serving layer if the framework points that way.
- [Local AI Package](../entities/tools/local-ai-package.md) - the bundled self-hosted stack, deployable to a cloud box you manage.
- [OpenRouter](../entities/tools/openrouter.md) - the managed path that keeps the decision reversible.
- [DigitalOcean](../entities/organizations/digital-ocean.md), [RunPod](../entities/tools/runpod.md) - where a controlled cloud instance actually lives.

## Sources

- [Local vs. Cloud LLMs/RAG - Let's FINALLY End this Debate](../sources/local-vs-cloud-llmsrag-lets-finally-end-this-debate.md) - "[0:00:16] a lot of what I've seen on YouTube is people saying that you have to go local with your AI solution or it is completely bust"
- [Local vs. Cloud LLMs/RAG - Let's FINALLY End this Debate](../sources/local-vs-cloud-llmsrag-lets-finally-end-this-debate.md) - "[0:00:32] there is a lot of nuance to this and that is what I need to dive into today"
