---
type: source
title: "Local vs. Cloud LLMs/RAG - Let's FINALLY End this Debate"
description: "Choosing between local/self-hosted and managed-service AI stacks is not a dogmatic all-or-nothing choice but a nuanced decision driven by whether you're building a POC, data sensitivity, capability needs, and scale."
youtube_id: W5X34-QmdEQ
url: https://www.youtube.com/watch?v=W5X34-QmdEQ
slug: local-vs-cloud-llmsrag-lets-finally-end-this-debate
published: 2024-09-11
duration: "0:10:15"
recency_rank: 166
raw: "../raw/local-vs-cloud-llmsrag-lets-finally-end-this-debate.md"
tags: [local-ai, rag, architecture, model-selection]
created: 2026-07-21
updated: 2026-07-21
---

# Local vs. Cloud LLMs/RAG - Let's FINALLY End this Debate

Cole pushes back on the popular YouTube narrative that you must go fully local or your AI solution is "bust," arguing the real answer is nuanced. He first lays out two parallel tech stacks with deliberately similar component names so a project can migrate easily between them: a local stack (Docker, Ollama for LLMs like Llama 3.1 70B, Qdrant for vectors, self-hosted Supabase for Postgres and auth with optional pgvector, n8n for workflows, Redis for caching) and a managed stack (Claude/GPT/Grok, Pinecone or Qdrant, hosted Supabase, Cloud n8n, Redis.io).

The core of the video is a decision tree. Start by asking if you are building just a proof of concept; if so, use managed services because they are far faster to stand up and easy to scrap, and you can mock or strip sensitive data during a POC. If it is production, ask whether the data is sensitive; sensitive data means go local to avoid feeding it to public LLMs that could train on and later regurgitate it. If data is not sensitive, ask whether you need the most powerful frontier models (for example Claude 3.5 Sonnet) for advanced tool calling or strict structured output, which are too compute-intensive to run locally and force managed services. Finally, ask about scale: below roughly 1,000 users managed services are often cheaper thanks to generous free tiers, while at thousands of users self-hosting becomes more cost-effective.

Cole emphasizes that a hybrid approach (for example a managed vector DB and caching but a self-hosted LLM) is very solid and common, but he frames the video around the two extremes to keep the picture simple. He closes by noting local models like Llama have gotten powerful enough that going local is increasingly a good default, and previews more local RAG and local LLM content.

## Concepts covered

- [Local & Self-Hosted AI](../concepts/local-ai.md)
- [Rapid Prototyping](../concepts/rapid-prototyping.md)
- [Agent Deployment](../concepts/agent-deployment.md)
- [Model Selection](../concepts/model-selection.md)
- [Retrieval-Augmented Generation (RAG)](../concepts/rag.md)

## Entities

- [Cole Medin](../entities/people/cole-medin.md)
- [Ollama](../entities/tools/ollama.md)
- [Llama](../entities/tools/llama.md)
- [Docker](../entities/tools/docker.md)
- [Qdrant](../entities/tools/qdrant.md)
- [Pinecone](../entities/tools/pinecone.md)
- [Supabase](../entities/tools/supabase.md)
- [n8n](../entities/tools/n8n.md)
- [Redis](../entities/tools/redis.md)
- [Anthropic](../entities/organizations/anthropic.md)
- [OpenAI](../entities/organizations/openai.md)

## Key moments

- **[0:00:16]** Frames the debate: YouTube pressure to go local or it's bust
- **[0:01:26]** Lays out the local tech stack (Docker, Ollama, Qdrant, Supabase, n8n, Redis)
- **[0:02:19]** Lays out the managed tech stack (Claude/GPT/Grok, Pinecone/Qdrant, hosted Supabase, Cloud n8n, Redis.io)
- **[0:03:34]** Notes the hybrid approach and why the video sticks to the two extremes
- **[0:04:19]** Decision tree Q1: Is it just a proof of concept? Use managed services
- **[0:05:48]** Decision tree Q2: Is your data sensitive? Go local
- **[0:06:34]** Decision tree Q3: Do you need the latest and greatest frontier model? Use managed
- **[0:07:37]** Decision tree Q4: Scaling past 1,000 users? Local is more cost-effective

## Transcript

[Raw transcript](../raw/local-vs-cloud-llmsrag-lets-finally-end-this-debate.md)
