Most of us have written the same glue between an LLM call, a vector store and half a dozen APIs more than once, and the pitch for any visual layer is that you stop rewriting it. If you are weighing that against another bespoke script, the first thing to check is not the node catalog, it is how the platform counts your work, because that number is what decides the bill at volume.
What n8n Is Underneath The Canvas
n8n is an open source automation platform built on a node based editor, where each node is an action: an API call, a transform, a message, a model inference. Workflows fire on webhooks, schedules, database changes or chat messages.
What separates it from the simpler tools is that you can drop into inline JavaScript or Python at any point in the graph. There are over 500 prebuilt integrations, and for anything without a node the HTTP Request node takes a raw curl command.
The Community Edition runs under a fair-code license. You self host it for free with unlimited executions, unlimited workflows and the full integration catalog. SSO, LDAP and audit logs are the parts behind a paid license.
The AI subsystem is built on LangChain, exposed as more than 70 visual nodes covering models, memory, chains, retrievers, vector stores, embeddings and output parsers.
The piece worth understanding is the AI Agent node. It is not a prompt template, it is a ReAct or OpenAI Functions agent with tool bindings, memory and an iterative reasoning loop. Any other n8n node can be bound as a tool, so the agent can query Postgres, hit an internal API or update a sheet as part of its own reasoning, and it picks which to call from context instead of following a path you hardcoded.
Model nodes cover OpenAI, Anthropic, Google, Mistral, Cohere and local models through Ollama. Swapping providers means swapping one node, the rest of the graph stays where it is.
Where The Execution Model Changes The Math
One execution is one complete workflow run, no matter how many nodes are in it. A 20 node workflow that runs once is one execution. Only successful runs count, so failed and test runs are free.
Put that next to per-task metering and the gap opens fast. Ten thousand runs of a ten step workflow is ten thousand executions here, and one hundred thousand tasks on a platform that bills each step. n8n Cloud Pro is 60 euros a month for ten thousand executions. Self hosting the same load costs a VPS, roughly four to seven dollars a month, because the execution ceiling disappears entirely. The full breakdown of the AI nodes, RAG setup and pricing tiers walks through where each path stops making sense.
Running The Whole Stack Locally
The Self-Hosted AI Starter Kit is a Docker Compose template that bundles n8n with Ollama for local models, Qdrant for vectors and PostgreSQL for data and conversation memory, in one command.
There are profiles for NVIDIA and AMD GPUs, a CPU only profile for testing, and on a Mac you run Ollama natively and point the containerized n8n at it. It is Apache 2.0, separate from the fair-code license on n8n itself, so there is nothing to negotiate around commercial use.
That gets you RAG pipelines, agents with persistent memory and local document processing without a single call leaving the machine, which is the part that matters if you are working under data sovereignty rules.
The Takeaway
Node count is the least interesting number on the page. What you actually want to know before committing a workflow to production is what the platform counts as one unit of work, when that count resets, and whether removing the ceiling is a matter of running it yourself. On n8n all three answers are good, and that is the reason it keeps showing up in agent stacks that started out as scripts.
