Self Hosted AI Starter Kit: Demo Stack vs Operated Cockpit

What the n8n self hosted AI starter kit includes from official docs, and a matrix that separates a local demo stack from an operated private cockpit.

A self hosted AI starter kit is usually a Docker Compose template. It boots a local AI and low-code lab. The n8n kit pairs self-hosted n8n with Ollama, Qdrant, and PostgreSQL. You can try private workflows on your machine1 2. n8n states the kit is for getting started. It is not fully optimized for production. You must secure and harden it before production use2. For a boutique firm, treat the compose file as a demo stack. Then score readiness for an operated private cockpit. That means named admins, credentials, failure watch, approvals, and exit.

What is the n8n self-hosted AI starter kit?

n8n’s Self-hosted AI Starter Kit is an open-source Docker Compose template. It is meant to start a local AI and low-code development environment quickly1. The same definition appears in n8n Docs under Deploy with the AI starter kit 2.

Official sources say the kit combines:

Self-hosted n8n, described as a low-code platform with over 400 integrations and advanced AI components1 2.
Ollama, to install and run local LLMs1 2.
Qdrant, an open-source vector store1 2.
PostgreSQL, for data storage in the stack1 2.

n8n’s own community reply is blunt. The starter kit is a convenient way to get started. It is not a final setup for every host path. And “the kit itself is just a compose file.”3 That line matters more than marketing screenshots. Compose gets containers talking. It does not name who patches the firm’s live agents. It does not name who holds client credentials. It does not name who watches a failed send on a Tuesday night.

n8n lists example build directions on the repo and docs. Examples include AI agents for scheduling appointments. They also include summaries of company PDFs. They include Slack bots for company communications and IT operations. They include private financial document analysis1 2. Those are starter directions. They are not a claim that any third-party product ships those flows ready for your clients.

What is in the self hosted AI starter kit on GitHub?

The main component is the Docker Compose file. It is pre-configured with network and storage settings. You do not need many extra installs after Docker is available1. Install steps on the official repo are:

Clone https://github.com/n8n-io/self-hosted-ai-starter-kit.git. Copy .env.example to .env. Then update secrets and passwords inside1.
Start with a hardware profile. Use gpu-nvidia. Use gpu-amd on Linux. Use plain docker compose up on Mac paths described in the README. Or use --profile cpu for everyone else1.
Open http://localhost:5678/ once to set up n8n1.
Open the included workflow. Use Chat on the canvas. On first run, wait if Ollama is still downloading Llama3.2. Check Docker console logs1.

The README tells you to keep work local. Use the Ollama node for the language model. Use Qdrant as the vector store. It also notes access to AI nodes once n8n is up. Those nodes include AI Agent, Text classifier, and Information Extractor1.

Mac users get an explicit GPU limit in Docker. On Apple Silicon you cannot expose the GPU to the Docker instance the same way. So the README offers CPU-only compose. Or you can run Ollama on the Mac and point n8n at host.docker.internal:114341.

Upgrade commands are also in the same README. They are profile-based pull and recreate sequences1. None of that is a production operating model. It is lab hygiene for a local stack.

A separate community package is coleam00/local-ai-packaged. It describes itself as another docker compose template for a local AI and low-code environment. It points back to the original n8n starter kit. It also adds other services in that fork’s own README4. Do not mix the two inventories. If a guide names Supabase, Open WebUI, Flowise, or similar, check which package it means. Check whether it is the n8n kit or the extended package4.

Forum and tutorial threads mostly restate the same install story. See Cloudron and the n8n community tutorial. The story is clone, compose, open the editor5 6. Useful for setup friction. Thin on firm-level ownership.

Demo stack versus operated private cockpit

Search results for this keyword cluster around install steps. Boutique firms need a second layer. Ask what must be true before client work touches the stack. Use this readiness matrix. Left column is what official n8n materials actually describe. Right column is the operator bar for a private firm cockpit. That bar is an ownership and duty split. It is not a product bill of materials.

Question	n8n self hosted AI starter kit (official)	Operated private cockpit (firm bar)
Primary job	Bootstrap a local AI and low-code development environment with Docker Compose1 2.	Run client-facing agents and workflows under named people, with inspectable access and an exit path
What ships in the box	Compose template plus n8n, Ollama, Qdrant, PostgreSQL as listed by n8n1 2.	Dedicated instance boundary, admin model, approval rules, credential ownership, support split. Stack parts are chosen and named in your design, not assumed from a public starter kit
Production stance	Designed to help you get started; not fully optimized for production; secure and harden before production use1 2.	Production means named patch owners, backup/restore drills, auth and network limits, and a written failure path before client data flows
Credentials	Copy `.env.example` and update secrets and passwords; keep model and vector choices local when you want local1.	Firm holds admin access and app secrets; model keys follow a BYOK or local policy you can explain to a client
Who watches failures	You watch Docker logs and first-run model download progress in the lab1.	Named on-call for workflow failures, approval queues, and integration errors; partner help only if contracted
Exit	Clone and customize a public compose repo; move only what you documented and exported1 3.	Export agents, permissions, and records without a vendor ticket drama; rebuild path known before go-live

AI Jungle OS is a done-with-you sovereign AI cockpit. It is a private box that stays yours. AI Jungle gives configuration and plan support. You operate agents and approvals. That is a product shape. It is not a claim that the cockpit ships the n8n starter kit. It is not a claim that it ships any named compose service from GitHub. For tenancy language next to this matrix, read private AI and box vs SaaS. For permissions and control boundaries, use security and governance. For how agent work is wired in practice, see AI agent orchestration; for model, tool, and connector boundaries, use the local AI agents decision guide. Plan options live on pricing without restating figures here.

See the AI Jungle OS cockpit

How do you run a self hosted AI starter kit example?

Official path, kept short:

Clone the n8n repo. Create .env from .env.example. Set secrets1.
Start the matching Docker Compose profile for your machine1.
Open http://localhost:5678/. Complete first-time n8n setup. Open the included workflow. Use Chat1.
On first chat, allow time for Ollama to finish downloading Llama3.2. Do this if logs show that still running1.

n8n Docs sends you to the same GitHub repository to clone and start2. Community tutorial threads walk the same “get started” motion on video6.

A third-party guide on Pinggy repeats the clone and compose flow. It then shows tunneling localhost n8n for remote access7. Treat remote exposure as a security decision, not a default. Opening a lab port to the internet without auth, TLS, and access limits is how a demo becomes an incident. n8n’s production note still applies. Secure and harden before production2.

If you refuse Docker, n8n staff on the community forum give another path. You can install the services listed in the compose file manually. You can configure them on their own. Use each project’s docs3. That path is more glue work. It is still a lab unless you add the operator row from the matrix above.

Can you run a self hosted AI starter kit on Ubuntu?

Yes, as a Linux host path covered by the official profiles. The README documents Nvidia GPU, AMD GPU on Linux, and CPU profiles. You use them via Docker Compose after you clone the repo and set .env1. Ubuntu is not a separate product edition. It is one common Linux base under Docker.

What Ubuntu does not give you for free:

Production hardening (n8n still says secure and harden before production)2.
A named backup owner for volumes that hold workflows, vectors, and database state
Client approval rules for irreversible actions
A support split when the person who ran docker compose up is offline

Community production threads ask what “secure and harden” means. They ask this when moving a chatbot-style flow toward a host like a cloud droplet. Answers there are operator compilations. They are not n8n guarantees8. Use them as checklists to challenge. Then verify against current n8n hosting docs and your own threat model.

Can you run a self hosted AI starter kit on Windows?

The official README centers on Docker Compose profiles. That includes CPU and GPU paths. It also covers browser access at http://localhost:5678/1. Windows users in the n8n community describe running the AI starter kit with Docker on Windows 11. They do this while they plan a later deploy8. That is evidence that people run the kit under Docker on Windows. It is not a promise about every Windows edition. It is not a promise about every GPU passthrough path. It is not a promise about every corporate laptop policy.

Practical filter for a firm laptop:

Docker available and allowed by IT
Secrets set in .env, not left as example defaults1.
No client production data until auth, network limits, and backups exist. Those must go beyond the lab note in n8n Docs2.

If Windows is only for demos, keep it that way. Move client workloads only when the operated-cockpit column is staffed.

What hardware does self-hosted AI need?

Official n8n kit materials do not publish a single universal SKU for every firm. They publish runtime profiles:

Nvidia GPU profile for Docker users who already can use an Nvidia GPU with Docker. The README points at Ollama’s Docker GPU notes if you have not done that before1.
AMD GPU profile on Linux1.
CPU profile when you are not on those GPU paths1.
Mac path with CPU compose or host-native Ollama. GPU expose into Docker is limited as described in the README1.

Third-party install posts sometimes add their own size suggestions. Those are not n8n kit guarantees. Pinggy states its own prerequisite wording. That wording includes a RAM suggestion and an optional Nvidia CUDA path7. Read that as one author’s guidance. Then size from the models you actually load. Also size from the concurrency you accept.

Reddit’s r/selfhosted thread frames a broader goal. That goal is private, resilient setups independent of big cloud vendors9. Homelab ambition is not the same as a consulting firm’s duty of care. Hardware that chats on a desk can still fail the cockpit column. That happens if nobody owns patches and approvals.

What is the best self-hosted AI model for a starter kit?

There is no single best model in the official kit docs. The n8n README’s first-run path references Ollama downloading Llama3.2 for the included workflow. It also tells you to use the Ollama node when you want the language model local1. That is a starter default for the demo workflow. It is not a ranking of every local model for every boutique use case.

Score models with operator questions, not hype:

Will this model run on the profile you can actually operate? That profile may be CPU, Nvidia, AMD, or host-native Ollama on Mac1.
Does client work need local weights? Or is a remote model with keys you hold acceptable under your private AI policy?
Who tests output quality before the agent may act on email, CRM, or files?
Can you change models without rewriting every workflow by hand?

“Best” for a firm is the model you can run, evaluate, and replace under a named owner. The starter kit only makes local Ollama easy to try1 2.

Is the self hosted AI starter kit enough for a boutique firm?

Enough to learn. Not enough to call the default compose file production-ready without hardening. That follows n8n’s own production note1 2.

Use this decision list before client data enters any starter stack:

Stay in demo mode when the goal is learning n8n AI nodes. Stay there when testing Ollama locally. Stay there when proving a PDF or chat flow on non-client samples1.
Extend the compose yourself when your team will own Docker, secrets, HTTPS, backups, upgrades, and on-call. Community production threads show people assembling that checklist in public. Treat them as homework, not a warranty8.
Buy a done-with-you private cockpit when you want a dedicated box and operator control. Do this when you do not want to turn the firm into a full-time stack lab. AI Jungle OS is built for that shape. You get a private box per client context. You operate agents and approvals. AI Jungle helps with configuration and plan. Details and options are on the cockpit home and pricing.

Keep the layers separate when you brief partners:

Model layer. Local via Ollama in the kit, or another path you choose1.
Orchestration layer. n8n workflows and AI nodes in the kit’s design1 2.
Firm layer. Who may approve irreversible actions. Who holds credentials. Who watches failures. What you can export on exit. That layer is not a line item in the starter kit README1 3.

Search will keep surfacing GitHub, docs, Cloudron, Reddit, and community threads. Install intent is loud1 2 5 9 3. Your advantage is answering the quieter question. After docker compose up, who runs the firm side?