n8n vs Temporal Quick Start and Deployment Guide

This is Part 5 of a 5-part series comparing n8n and Temporal for AI workflow orchestration. Read Part 1 for the overview, Part 2 for the head-to-head comparison, Part 3 on error handling, and Part 4 on when to choose Temporal.

Quick Start Guide: n8n Self-Hosted (Docker Compose)

Create docker-compose.yml:

version: "3.8"

services:
  n8n:
    image: docker.n8n.io/n8nio/n8n:latest
    restart: always
    ports:
      - "5678:5678"
    environment:
      - N8N_USER_MANAGEMENT_DISABLED=false
      - DB_TYPE=postgresdb
      - DB_POSTGRESDB_HOST=postgres
      - DB_POSTGRESDB_DATABASE=n8n
      - DB_POSTGRESDB_USER=n8n
      - DB_POSTGRESDB_PASSWORD=<DB_PASSWORD>
      - EXECUTIONS_MODE=regular
    volumes:
      - n8n_data:/home/node/.n8n
    depends_on:
      - postgres

  postgres:
    image: postgres:16-alpine
    restart: always
    environment:
      - POSTGRES_USER=n8n
      - POSTGRES_PASSWORD=<DB_PASSWORD>
      - POSTGRES_DB=n8n
    volumes:
      - postgres_data:/var/lib/postgresql/data

volumes:
  n8n_data:
  postgres_data:

docker compose up -d
# Access at http://localhost:5678

Configure user management via n8n UI after first start, or use an external auth proxy for SSO.

For production, add a reverse proxy (Traefik or NGINX), enable HTTPS, and switch EXECUTIONS_MODE to queue with Redis for horizontal scaling. See the n8n.io documentation for advanced configurations.

Quick Start Guide: Temporal Self-Hosted (Docker Compose)

Temporal provides a development Docker Compose with the server, PostgreSQL, Elasticsearch, and Web UI:

# Clone the official docker-compose repo
git clone https://github.com/temporalio/docker-compose.git
cd docker-compose

# Start the default stack (PostgreSQL + Elasticsearch)
docker compose -f docker-compose-postgres.yml up -d

# Verify services
temporal server health
# Expected: SERVING

The stack exposes the Frontend at localhost:7233 and the Web UI at http://localhost:8080. To run a workflow, you’ll need a worker:

import asyncio
from datetime import timedelta
from temporalio import workflow, activity
from temporalio.client import Client
from temporalio.worker import Worker
from temporalio.common import RetryPolicy

@activity.defn
async def call_llm(prompt: str) -> str:
    # Your LLM API call here
    return f"Response to: {prompt}"

@workflow.defn
class AIWorkflow:
    @workflow.run
    async def run(self, prompt: str) -> str:
        return await workflow.execute_activity(
            call_llm,
            prompt,
            start_to_close_timeout=timedelta(seconds=30),
            retry_policy=RetryPolicy(maximum_attempts=3),
        )

async def main():
    client = await Client.connect("localhost:7233")
    worker = Worker(
        client,
        task_queue="ai-queue",
        workflows=[AIWorkflow],
        activities=[call_llm],
    )
    await worker.run()

if __name__ == "__main__":
    asyncio.run(main())

For production Kubernetes deployments, use the Temporal Helm charts. Run separate worker deployments per task queue to isolate failure domains. See the Temporal documentation for SDK references and production best practices.

FAQ

Which is better: n8n or Temporal for AI workflows?

Neither is universally better, it depends on your team’s skills and workflow requirements. n8n is the better AI workflow orchestrator for teams that need visual prototyping and native AI node support. Temporal is the better workflow engine for teams building mission-critical, long-running systems that must survive infrastructure failures. Choose n8n for speed; choose Temporal for durability.

Is n8n production-ready for AI workflows?

Yes, with proper configuration. n8n runs production workloads for thousands of teams. Switch from SQLite to PostgreSQL, enable queue mode with Redis for concurrency, configure authentication, and monitor execution logs. It’s not “set and forget” at scale, but it’s production-capable when configured correctly. See our n8n deployment guide for a production checklist.

What is Temporal used for in AI systems?

Temporal is a durable execution platform for long-running AI workflows in distributed systems. Common use cases include multi-step LLM agent orchestration where each reasoning step must be reliably tracked and resumed, document processing pipelines that span hours, and human-in-the-loop approval workflows that may wait days for a response.

Can n8n handle AI workflows without Temporal?

Yes. n8n has native AI nodes including LLM Chat Model, AI Agent, Vector Store, and LangChain integration for building RAG pipelines and multi-agent systems. The limitation is not capability but durability, n8n workflows are less resilient to long outages compared to Temporal’s replay model. For AI workflows that don’t require multi-day durability, n8n is fully capable.

Does Temporal support AI workflows without a visual interface?

Yes, but you build AI logic entirely in code. Temporal has no built-in AI nodes. You write activities that call LLM APIs, query vector databases, or run inference. This gives you full control over retry logic, caching, model fallbacks, and versioning. Temporal’s Python SDK is particularly well-suited for AI workflow orchestration.

Should I use n8n or Temporal for my AI project?

Choose n8n if your AI project requires fast visual development, short-lived workflows (seconds to minutes), and your team includes non-developers. Choose Temporal if your AI project is mission-critical, requires multi-hour or multi-day execution, and your team is comfortable with code-first development. A hybrid architecture, n8n at the edges for integrations, Temporal at the core for durable orchestration, is often the pragmatic path for growing teams.

Can you run n8n and Temporal together?

Yes, and many teams do. A common pattern uses n8n for webhook ingestion, quick API integrations, and Slack notifications, workflows that change frequently and are maintained by ops teams. Temporal handles the core: long-running document processing, multi-step AI agent reasoning, and payment-adjacent workflows that require durability guarantees. n8n signals Temporal to start work, and Temporal signals back when complete.

Conclusion

The n8n vs Temporal debate is not about finding a single winner, it’s about matching the right workflow orchestrator to your constraints.

n8n excels at velocity. It gets AI-powered automations running quickly with minimal code. The native AI nodes and visual interface mean non-developers can build workflows too. If you’re building internal automations or AI agent prototypes that change weekly, n8n is the right starting point.

Temporal excels at reliability. It ensures complex, long-running workflows complete correctly even when infrastructure fails. If you’re building platform infrastructure or financial workflows where a missed step costs real money, Temporal is the workflow engine to invest in.

For most growing teams, the answer is not one or the other, it’s both. Use n8n where speed matters and Temporal where durability matters. The hybrid AI workflow architecture gives you the best of both worlds.

For a deeper dive into production deployment, see our n8n deployment guide and Temporal deployment guide. Also check the n8n.io website for community workflows and the Temporal documentation for SDK tutorials.

Update (2025-05-08): Compared n8n 1.84.x and Temporal Server 1.27.x. n8n’s AI Agent node and Temporal’s Python SDK were the versions evaluated.

Parts in this series: ← Part 1 | ← Part 2 | ← Part 3 | ← Part 4 | Part 5