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You can run n8n on infrastructure you control—on-premises or in a private cloud—and use it to orchestrate hosted AI APIs or local models. For most first deployments, Docker Compose is the clearest repeatable setup: budget at least 4 GB of RAM and 2 vCPUs, persist /home/node/.n8n, put the editor behind HTTPS, and expose only the webhook routes you actually need.
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From there, choose between provider APIs and a local stack. n8n’s official Self-hosted AI Starter Kit combines n8n with Ollama, Qdrant and PostgreSQL for local model serving, vector search and persistence. Build workflows in stages, validate model output before it reaches another system, and check the Sustainable Use License before turning your installation into a customer-facing service.
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What self-hosting n8n actually gives you
Self-hosting means the n8n application, its database, execution data and credentials run on servers you administer rather than on a vendor-operated n8n instance. Those servers can be in your own office, a data center, or a private-cloud virtual machine. n8n’s hosting documentation explicitly supports self-hosting with Docker Compose, a one-line setup and other deployment methods.
The control boundary matters for AI workflows. Input documents, prompts, model responses, API tokens and execution history pass through the systems you choose. That can help with data-residency or private-network requirements, but it also makes you responsible for patching, backups, access control, TLS, monitoring and incident response.
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When your own server is a good fit
- You need workflows to reach internal databases or services that should not be exposed to the public internet.
- You want predictable control over where execution data and credentials are stored.
- You need to run a local model or keep retrieval data inside a private network.
- You are comfortable maintaining a Linux host, Docker services, DNS, TLS and backups.
When a simpler arrangement may be better
A managed n8n deployment removes much of the server maintenance. Self-hosting is not automatically more secure: an unpatched, publicly reachable editor with weak authentication can be riskier than a professionally operated service. Choose self-hosting because you need the control, integration reach or local-processing option, not merely because the software can be installed for free.
Choose the deployment boundary before installing
Decide first whether the instance must receive requests from outside your network. This single choice determines your DNS, firewall, reverse-proxy and authentication work.
Private-only instance
A private-only instance is reachable through a LAN, VPN or private-cloud network. It is appropriate when workflows are triggered by internal schedules, queues or applications. Keeping the editor and webhook endpoints private reduces the exposed attack surface, but internal users and services still need authentication and least-privilege access.
Public webhook endpoint
External services often require a public HTTPS callback. In that design, publish only the webhook routes required by those integrations. Keep the editor and administration paths restricted whenever possible, terminate TLS at a maintained reverse proxy, and apply authentication, rate limits and network rules appropriate to the integration. A public webhook is an intentional exception to a private network, not a reason to publish the entire n8n interface.
Hosting options
| Option | Advantages | Responsibilities | Best for |
|---|---|---|---|
| On-premises server | Direct control of network, storage and physical location | Hardware maintenance, power, connectivity, backups and disaster recovery | Organizations with an existing private infrastructure |
| Private-cloud virtual machine | Fast provisioning, flexible resizing and a routable network | Operating-system updates, firewall rules, identity, storage and provider controls | Teams that want control without buying hardware |
| Single Docker host | Small operational footprint and a quick start | All application, database and host administration on one machine | Personal automation and modest internal workloads |
| Compose stack | Separate services can be added consistently | Service networking, persistent volumes, upgrades and resource allocation | AI workflows using PostgreSQL, Ollama, Qdrant or the Assistant sandbox |
Capacity and software prerequisites
Documented baseline
n8n’s current Docker Compose guide lists Docker Engine, Docker Compose v2, at least 4 GB of RAM and 2 vCPUs for the documented setup. Treat those figures as a baseline for the Compose installation, not as a performance guarantee. Workflow concurrency, large binary files, database growth, embeddings and model inference can all require more capacity.
| Resource | Documented baseline | Why you may need more |
|---|---|---|
| RAM | At least 4 GB for the Compose guide | The Assistant sandbox, concurrent executions, large payloads, vector indexing and local models add memory pressure. |
| CPU | At least 2 vCPUs for the Compose guide | Parsing, concurrent workflows, embedding generation and model inference compete for CPU time. |
| Storage | Persistent storage for /home/node/.n8n |
Execution history, binary data, logs, database files and model assets grow over time. |
| Networking | Docker networking plus access to required integrations | Public webhooks need DNS, HTTPS and an ingress path; local AI services need reachable internal service names or addresses. |
The Assistant sandbox may need additional headroom beyond the baseline. A local model also has its own memory and compute requirements; the n8n baseline does not describe a universal hardware size for every model.
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Install the host tools
- Provision a supported Linux host or another environment where Docker Engine can run reliably.
- Install Docker Engine and Docker Compose v2 from the maintained packages for that operating system.
- Create a dedicated project directory for the Compose files and environment configuration. Restrict that directory to administrators because environment files can contain secrets.
- Decide where the host will store the persistent n8n data directory before starting the container.
Use the Compose project as the unit you can stop, back up, update and restore. Keep configuration under version control only when secrets are excluded; store secret values in a protected secret-management process or an access-controlled environment file.
Deploy n8n with Docker Compose
The exact Compose file varies with the database, ingress and optional AI services you select. The operational sequence is stable.
- Create the project. Place the official n8n Compose definition and its environment settings in the project directory. Set the public URL and timezone deliberately; webhook URLs are generated from the configured address, so an incorrect hostname can break callbacks.
- Attach durable storage. Map a host volume or named volume to
/home/node/.n8n. Do not rely on a container’s writable layer. This directory contains essential user data, including the encryption key used for credentials, even when another database is configured. - Set access controls before exposure. Configure the editor URL, TLS termination and administrative authentication before opening the service to users or external networks.
- Start the stack. From the project directory, run
docker compose up -d. Check startup output withdocker compose logs -fand confirm that the container reaches a healthy running state. - Complete the first-run setup. Open the editor through the protected HTTPS address, create the administrator account and confirm that a test workflow can be saved and executed.
- Verify persistence. Stop and start the project with
docker compose stopfollowed bydocker compose start. The account, workflow and credential metadata should remain available. If they disappear, stop and correct the volume mapping before importing real data.
A single n8n service is the easiest starting point. Add PostgreSQL, Ollama, Qdrant or the Assistant sandbox only after the base instance is stable, so a failure in an optional service does not obscure a basic deployment problem.
Protect data, credentials and the editor
Back up the right data
Back up the persistent /home/node/.n8n directory and any external database used by the deployment. The directory includes user data and the encryption key for credentials. A database dump without that directory may leave you with workflow records that cannot decrypt their stored secrets; a directory backup without the external database may omit execution or application data.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchBefore upgrades or migrations, stop writes, create a tested backup and record which Compose definition and environment settings produced it. Store backups separately from the server and periodically perform a restore to an isolated instance. A backup that has never been restored is only an assumption.
Lock down editor access
- Put the editor behind HTTPS with a certificate that clients can validate.
- Restrict administrative access to a VPN, private network or approved identity boundary where practical.
- Use individual accounts and the narrowest permissions your n8n edition and organizational process support.
- Do not place credentials in workflow text, prompts, screenshots or unprotected environment files.
- Review who can create credentials, edit workflows and inspect execution data.
Understand credential handling
n8n states that credentials are loaded into the execution environment of the instance. Anyone who can administer the host, alter workflows or execute arbitrary code in that environment may be able to reach sensitive material. Protect the operating system, Docker socket, Compose files and backups with the same seriousness as the editor.
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Limit webhook and outbound risk
Expose only the webhook paths required by a workflow. Validate signatures or shared secrets supplied by the originating service, reject unexpected methods and payloads, and apply rate limits at the ingress layer. Keep internal administration routes off the public listener. n8n’s policies prohibit unauthorized access and server-side request forgery; your network design should reinforce those requirements with egress filtering and allowlists where feasible.
Choose hosted AI APIs or a local model
| Approach | How it works | Strengths | Trade-offs |
|---|---|---|---|
| Hosted model API | n8n sends prompts and inputs to a provider through stored credentials and receives model output. | Little local compute, quick model changes and less model-serving maintenance. | Data leaves the server for the provider, usage depends on network availability and provider terms, and API costs or limits apply. |
| Local model | A model server runs inside your infrastructure and n8n calls it over the private network. | Supports private-network and data-residency goals and can keep prompts and responses inside your environment. | You maintain model files, updates, capacity, availability and quality evaluation; inference can require substantial resources. |
Use provider credentials
- Create the required credential in n8n using the provider’s current authentication method.
- Store the secret in n8n’s credential system rather than embedding it in a node expression or prompt.
- Run a small test with non-sensitive input and confirm the returned structure, timeout behavior and error response.
- Add usage limits, retries and logging that avoid recording raw secrets or unnecessary personal data.
The credential lets the workflow call the provider, but it does not make the provider part of your private network. Define what data may leave the server and remove fields that the model does not need.
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Run the Self-hosted AI Starter Kit
n8n’s official Self-hosted AI Starter Kit documents a local stack containing:
- Ollama for serving local models.
- Qdrant for vector search and retrieval.
- PostgreSQL for persistent application data.
- n8n for workflow orchestration.
Deploy the kit as its documented Compose stack, then verify service-to-service networking before building a complex workflow. Download a model in Ollama, confirm that n8n can reach the model endpoint, create a Qdrant collection for the embedding dimension you selected, and verify PostgreSQL persistence. Keep model files and vector data on durable storage; otherwise a container replacement can remove the local assets your workflow depends on.
The starter kit is an operational starting point, not a promise that every model fits the 4 GB/2-vCPU baseline. Size the host for the model, concurrent requests and indexing workload, and monitor memory before enabling production traffic.
Build an AI workflow in safe, testable stages
Do not begin with an autonomous agent that can write to every connected system. Make each boundary explicit and testable.
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- Trigger. Start with a schedule, an authenticated webhook or an internal event. Capture an execution identifier so failures can be traced.
- Normalize input. Convert incoming fields into a predictable object, enforce maximum lengths and discard fields the model does not need. Treat webhook content as untrusted input.
- Call the model. Use a narrowly scoped system instruction, define the expected output and set a timeout appropriate to the provider or local service.
- Validate structured output. Parse the response and check required fields, data types, allowed values and length limits. A response that is not valid JSON or fails a schema check should follow an error path, not proceed to an external action.
- Invoke tools or application APIs. Pass only validated fields to the next node. Use separate credentials for separate systems and grant each one the minimum operation it needs.
- Add retries deliberately. Retry transient network or rate-limit failures with a bounded count and delay. Do not blindly retry non-idempotent writes, payments, account changes or messages.
- Log safely. Record status, timing, model identifier and an execution reference. Redact tokens, credentials and sensitive payloads from logs and retained execution data.
- Require human approval for consequential actions. Route uncertain classifications, destructive changes, external communications or high-impact decisions to a person before the final API call.
Design the model contract
Describe the output as a contract rather than asking for an unstructured paragraph. For example, require an object with an approved category, a short explanation and a confidence value, then reject anything outside the permitted categories. Keep the parser and validator immediately after the model node so an invalid response cannot silently reach a tool node.
Test with representative but non-sensitive data
Build a fixture set that includes normal inputs, empty fields, maximum-length text, malformed webhook bodies, ambiguous requests and provider errors. Compare the parsed result with expected outcomes, then test the complete path through every tool while using sandbox accounts or read-only credentials. The official material establishes the need for secure operation but does not prescribe one universal test plan, so document the cases that matter to your workflow.
Pick a data and service architecture
| Decision | Simple deployment | Expanded deployment | Choose expanded when |
|---|---|---|---|
| Application layout | One n8n service and its local persistent state | Compose-managed n8n plus PostgreSQL, Ollama, Qdrant or the Assistant sandbox | You need local AI, vector retrieval, a separate database or isolated supporting services. |
| Database | SQLite-style local data for a small single-instance workload | PostgreSQL-backed deployment | Durability, operational separation or the starter kit’s documented architecture matters. |
| Network | Private editor and internal triggers | Restricted public webhook ingress plus private administration | An outside service must initiate executions. |
| Compute | Documented 4 GB RAM and 2 vCPUs baseline | Additional CPU, RAM and storage sized for services and concurrency | Local inference, indexing, sandbox execution or many simultaneous runs create contention. |
Start with the smallest architecture that meets the data and integration requirements. Moving from a local database to PostgreSQL or adding a model server later is easier when workflows already have clear inputs, outputs and backups.
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Updates and change control
Before upgrading n8n or an accompanying service, capture a backup of /home/node/.n8n and the external database, record the current image versions and review release notes. Apply upgrades first to an isolated copy when the workflow is business-critical. Keep the Compose definition and environment changes reviewable so you can reproduce or roll back a known configuration.
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- Container health and restart counts.
- CPU, memory and disk consumption, especially during model inference and vector indexing.
- Workflow failure rate, duration and retry counts.
- Webhook response codes and latency.
- Database and persistent-volume growth.
- Authentication failures and unexpected administrative activity.
Set an alert before disk space is exhausted. A full volume can prevent executions, database writes and useful logging at the same time.
Rotate and revoke credentials
If a token appears in a log, workflow export, backup or unauthorized execution, revoke it at the provider, create a replacement and update the n8n credential. Review which workflows can access the credential and remove unused entries. Rotation is easier when each integration has its own credential instead of one shared master token.
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Licensing when you build for clients
Without a license key, n8n runs as the free Community edition. n8n’s licensing FAQ states that if you build and run workflows for clients on your own instance, you can use the Community edition for free.
That statement is narrower than a blanket permission to sell n8n as a hosted product. Before you offer customers a service in which they directly use an n8n-powered builder, receive a shared automation platform, or access n8n functionality as part of your commercial product, read the current Sustainable Use License scenario and obtain clarification when the arrangement is ambiguous. The relevant distinction is who operates the instance, who gets direct product access and whether you are providing a hosted automation service rather than delivering workflows that you operate for a client.
Troubleshoot common self-hosted failures
Workflows or credentials vanish after a restart
The container is probably using ephemeral storage or the volume is mounted at the wrong path. Stop the project, inspect the Compose volume mapping and ensure the host or named volume is attached to /home/node/.n8n. Restore the most recent verified backup only after confirming the encryption key and database belong to the same instance.
The server runs out of memory
Check whether the Assistant sandbox, a local model, concurrent executions or vector indexing is consuming the available RAM. Reduce concurrency, pause optional services and increase the host capacity. The 4 GB figure is the Compose guide’s baseline, not a ceiling for AI workloads.
A webhook returns a timeout or cannot connect
Confirm that the configured public URL matches DNS and the reverse proxy, that the certificate is valid, and that the firewall forwards only the intended webhook path. If the workflow is private-only, an external service will not be able to reach it without a deliberate VPN or ingress design.
Credentials fail after migration
Check that the original /home/node/.n8n data and its encryption key were migrated with the database. Recreating a container or changing the key without a controlled migration can leave encrypted credentials unreadable. Restore the matched backup rather than copying only workflow definitions.
The local model is unreachable
Verify that the Ollama service is running, the n8n container can resolve and reach its internal address, and the model is present. Check service logs and resource usage before changing workflow logic. A successful n8n startup does not prove that every supporting AI service is healthy.
Quick Recap
A practical launch checklist
- Host selected: on-premises or private cloud, with a documented owner.
- Docker Engine and Docker Compose v2 installed.
- At least 4 GB RAM and 2 vCPUs allocated for the documented Compose baseline, with extra capacity planned for the Assistant sandbox or local models.
- Durable storage mapped to
/home/node/.n8n. - Backups include that directory, its encryption key and any external database; a restore has been tested.
- Editor access is protected by HTTPS and restricted administration.
- Webhook exposure is limited to required routes with authentication and network controls.
- AI choice documented: hosted provider credentials or the local Ollama, Qdrant and PostgreSQL stack.
- Model output is schema-validated before tools or application APIs run.
- Retries, logging, redaction and human approval are defined for consequential actions.
- Monitoring covers failures, resource pressure, disk growth and suspicious access.
- The Sustainable Use License has been checked for any client-facing or hosted-service arrangement.
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