I wanted to share a project I’ve been building over the past few months to address one of the biggest friction points I personally faced when using n8n: the DevOps overhead of self-hosting and scaling.
While running n8n locally or managing a single self-hosted instance is straightforward, managing multiple production instances across clients becomes complex. Each instance requires separate server configuration, SSL, environment variables, Redis/Postgres, deployments, monitoring, and updates making manual management time consuming and difficult to scale.
To solve this, I built Cuebic AI to manage multiple clients self-hosted n8n instances from a single dashboard, making it easy to monitor and manage everything in one place.
Why I Built It & What It Does
The goal was to eliminate the pain of manually managing every n8n environment SSHing into servers, updating versions, configuring environments, and checking instances one by one.
UI-Based n8n & Queue Mode Configuration: Easily configure execution modes, worker concurrency, auto-cleanup settings, data retention, log levels, and environment variables directly inside a web dashboard before or after launch.
Dedicated VM Isolation: Every deployment runs on its own isolated Vultr virtual machine (not a shared multi-tenant database), ensuring complete data privacy and full environment control.
Automated Infrastructure: Automates domain configuration, SSL certificate provisioning and renewal, PostgreSQL/Redis setup, Fail2Ban security, and firewall rules eliminating manual server configuration.
Health & Resource Monitoring: Tracks CPU, RAM, disk usage, system services, and domain/SSL status in real time, with historical performance charts and automated alerts when something goes down or requires attention.
Disclaimer: Cuebic AI is an independent third-party hosting platform and is not officially affiliated with or endorsed by n8n.
I’d love to hear from other self-hosters: What are the biggest pain points you run into when managing multiple clients n8n instances or Queue Mode workers? And what would you like to see the UI handle next?
The multi-client part is the real problem, and I do not think it is dashboard. It is telling working apart from quiet.
I run one box and even there it is 7 containers, 1.8GB of old build cache that filled the disk last week, and 29 lines of proxy config that were never in git. Across ten client instances that is ten of everything, and none of it errors.
The question I could not answer on my own engine was which workflows ran and did nothing. A quiet day and a broken condition look identical unless you know what a normal run produces for that client. I build a hosted tool so I am biased, but that is the number I would put on the dashboard before any other.
Hey @Aghassi, great feedback you’ve described the exact multi client headaches we built Cuebic AI to solve.
Every client gets their own dedicated VM instance. We automate Nginx proxy routing, SSL certificate management, auto-renewals, and execution/cache cleanup behind the scenes so no untracked configs or full-disk surprises.
Server health (CPU/RAM/uptime) won’t tell you if a webhook failed silently. Right now, our 24/7 monitoring covers infrastructure and core service health (n8n, Postgres, Redis, SSL), but we are currently working on implementing workflow polling and execution anomaly detection to catch those “silent” failures.
Execution anomaly detection is the right next thing, and there is one shape it will not catch, which is the shape that got me.
My 27 branches ran on schedule every day and were always green. The cadence was normal. Nothing about frequency, duration or error rate was anomalous. What was wrong was inside each run: the condition could never match, so the step was skipped and the run finished COMPLETED having done nothing. Anomaly detection on run counts needs a run to go missing, and mine never did.
The other one worth deciding early is what polling proves. It tells you the run happened, not that the outcome landed. Someone in another thread called it execution evidence versus outcome evidence. On a client dashboard that is the difference between “their workflow ran” and “their leads actually arrived”, and only the second one is what they are paying you for.
Privacy Boundary: Because Cuebic AI operates on a zero-access model with dedicated VMs, our system never reads or inspects user workflow data or payloads.
The privacy boundary makes sense and I would keep it. I think it constrains the anomaly detection more than the line suggests, though, and there may be a way through.
Error Trigger and Stop & Error both need something to throw. A skipped branch throws nothing, and that is the whole failure. My 27 ran green for weeks with error handling wired up, because there was no error to catch. Stop & Error works only where someone already anticipated that exact condition and wrote the node, per workflow, forever — which is the objection that started this thread: per-step
assertions do not scale past a handful.
The middle path might be counts without retention. Per-node item counts exist in the execution data. Deriving “12 entered stage 2, 0 left it” means touching that data in transit but keeping only 2 integers, which is a different privacy claim from reading and storing payloads. Whether that fits a zero-access promise is a real design question, not a rhetorical one. If it does, the boundary becomes counts versus content rather than infrastructure versus business logic - and outcome detection lands on your side of it.
Project Website: https://cuebicai.com/