Tag
A local LLM is not free; it trades a per-token API bill for a mostly fixed monthly cost. This reproducible break-even model loads both columns (hardware, electricity, and token volume) and shows exactly where local stops costing more than the API.
Most "local RAG" setups still send your documents to a cloud embeddings API. This guide closes every leak: local embeddings via Ollama, a self-hosted Qdrant or PGVector store, and a local answering model, all in n8n.
Choosing between n8n RAG embeddings comes down to four verifiable axes: dimensions, price per 1M tokens, max input context, and cloud vs local. This guide compares OpenAI, Gemini, and Ollama options so you pick the one you can live with.
Local RAG with Ollama lets you ask questions across hundreds of private documents without uploading a single file to the cloud. This guide covers three paths from 20-minute desktop setup to n8n pipelines, all at $0 running cost.
The credential test fails because n8n looks inside its container, not at Ollama on your host. This guide shows the exact Base URL for every setup: native, Docker Desktop, and Linux server.
Most "AI stack" posts hide the bill. Mine runs under $10/month. Here is every tool, the exact tier, the real cost, and the job each layer does for my one-person content business.
Running a local LLM cuts your AI API bill to $0 and keeps client data off third-party servers. This breakdown of Ollama, LM Studio, and Jan tells you exactly which tool fits a solo operator's workflow.
Most "free AI IDE" lists mix up four completely different things. This guide splits 11 tools into truly free, BYOK, freemium, and trial-only, so you know exactly what you're getting before you build.
