AI TRAINING
n8n Self-Hosted AI Workflows for Engineering Teams
Deploy, secure, and scale n8n on your own infrastructure to power production-grade AI automation workflows.
What it covers
This practitioner-level bootcamp teaches engineering and IT teams how to self-host n8n on Docker or Kubernetes, configure secrets management and authentication, and build robust AI-integrated workflows using LLMs, vector stores, and HTTP nodes. Participants work through hands-on labs covering queue-mode execution, worker scaling, and high-availability setups. By the end, teams can operate a production-ready n8n instance and maintain a library of reusable AI workflow templates. The programme balances architectural concepts with live coding sessions at a 30/70 theory-to-practice ratio.
What you'll be able to do
- Deploy a production-ready n8n instance on Docker Compose or Kubernetes with persistent storage and TLS termination
- Configure encrypted credential storage, environment-based secrets, and role-based access control
- Build and test a multi-step AI workflow that chains an LLM call with a vector store retrieval and a downstream HTTP action
- Configure queue mode with Redis workers to handle concurrent workflow executions without data loss
- Implement monitoring dashboards and alerting for workflow failures using n8n execution logs and external observability tools
Topics covered
- Self-hosting n8n on Docker Compose and Kubernetes (Helm charts)
- Environment variables, secrets management, and credential encryption
- Queue mode with Redis and horizontal worker scaling
- High-availability deployment patterns and health checks
- Building AI workflows: LLM nodes, Langchain agents, and vector store integrations
- Webhook security, API authentication, and network isolation
- Error handling, retry logic, and workflow observability (logs + metrics)
- CI/CD for workflow version control and automated deployment
Delivery
Delivered as a 2–3 day in-person or remote bootcamp with live instructor-led sessions. Each module includes a hands-on lab on a shared cloud sandbox or participants' own infrastructure. Materials include pre-built workflow templates, a Helm values reference, and a secrets management checklist. Remote delivery uses breakout rooms for pair-programming labs. Minimum 70% hands-on time. A follow-up async Q&A session is recommended one week after delivery.
What makes it work
- Standing up a dedicated staging environment that mirrors production before any live workflows are deployed
- Establishing a workflow naming convention and tagging system so the library remains navigable as it grows
- Integrating n8n execution logs into the team's existing observability stack (e.g. Grafana, Datadog) from day one
- Assigning a workflow owner per domain who is responsible for maintenance and incident response
Common mistakes
- Running n8n in a single-process mode in production, causing workflow queues to block under load
- Storing API keys and credentials as plain-text environment variables instead of using n8n's encrypted credential store
- Skipping webhook authentication and exposing n8n endpoints to the public internet without IP allowlisting
- Treating n8n workflow files as undocumented artefacts instead of version-controlling them alongside application code
When NOT to take this
A small startup with no dedicated DevOps resource that wants to get started with automation quickly, they are better served by n8n Cloud or a fully managed iPaaS like Make.com until they have the infrastructure maturity to self-host responsibly.
Example providers
This page describes a training topic. It gives no price or duration: those depend on the provider and your team. The free diagnostic tells you which trainings your team needs first.