---
title: "Deploy Faktory"
description: "Faktory 1.10: language-agnostic background job server with a web UI."
category: "Queues"
url: https://railway.com/deploy/faktory
---

# Deploy Faktory

Faktory 1.10: language-agnostic background job server with a web UI.

**[Deploy Faktory on Railway](https://railway.com/template/faktory)**

Machine-readable deploy manifest (JSON, validated by TemplateCI): https://railway.com/deploy/faktory/manifest.json

- **Creator:** Agaz Self-Host
- **Category:** Queues

## Template content

### faktory https://github.com/contribsys.png

- **Image:** contribsys/faktory:1.10.0
- **Start command:** `/faktory -b [::]:7419 -w [::]:7420 -e production`
- **Public domain:** Yes

## Documentation

# Deploy and Host Faktory on Railway

Faktory is a language-agnostic background job server from the author of Sidekiq. Producers push jobs to named queues, and workers in Ruby, Go, Python, Node.js, Rust or other languages fetch and acknowledge them. It handles retries with backoff, scheduled jobs and a dead set, and comes with a web dashboard.

## About Hosting Faktory

This template runs the official `contribsys/faktory:1.10.0` image as one service in production mode, where every client and the dashboard need the generated password. Workers in the same project connect over the private network on port 7419, and a TCP proxy lets workers outside Railway connect too. The web dashboard is on a public HTTPS domain and asks for the same password with any username. Queues live in Faktory's embedded Redis on a Railway volume, so pending jobs survive redeploys. The image runs as root and has no health endpoint, so the service relies on the restart policy.

## Common Use Cases

- Background jobs shared between services written in different languages
- Scheduled and retried work such as emails, webhooks and report generation
- Moving job processing out of a web service without running Redis yourself

## Dependencies for Faktory Hosting

- `contribsys/faktory:1.10.0` (official image)
- A Railway volume at `/var/lib/faktory` for the job store

### Deployment Dependencies

- [Faktory wiki](https://github.com/contribsys/faktory/wiki)
- [Faktory 1.10.0 release](https://github.com/contribsys/faktory/releases/tag/v1.10.0)
- [Faktory client libraries](https://github.com/contribsys/faktory/wiki/Related-Projects)

### Implementation Details

| Service | Source | Networking | Storage |
| --- | --- | --- | --- |
| faktory | `contribsys/faktory:1.10.0` | dashboard on HTTPS, jobs on private port 7419 + TCP proxy | volume at `/var/lib/faktory` |

| Variable | Purpose |
| --- | --- |
| `FAKTORY_PASSWORD` | Generated password for clients and the dashboard |
| `FAKTORY_URL` | `tcp://:@faktory.railway.internal:7419` for workers in the project |
| `FAKTORY_PUBLIC_URL` | Same, through the TCP proxy |

Most client libraries read `FAKTORY_URL` directly, so `FAKTORY_URL=${{faktory.FAKTORY_URL}}` on a worker service is usually all it needs.

The start command binds both ports to `[::]`, so the private network (IPv6) and the dashboard (IPv4) both work. Enterprise features such as batches and job tracking require a Faktory Enterprise license and are not included.

This is a community-maintained deployment package and does not imply affiliation with or endorsement by Contributed Systems.

## Why Deploy Faktory on Railway?

Railway is a singular platform to deploy your infrastructure stack. Railway will host your infrastructure so you don't have to deal with configuration, while allowing you to vertically and horizontally scale it.

By deploying Faktory on Railway, you are one step closer to supporting a complete full-stack application with minimal burden. Host your servers, databases, AI agents, and more on Railway.


## Similar templates

- [Redpanda](https://railway.com/deploy/redpanda-1) — Redpanda 26.2: Kafka-compatible streaming, single node, with Kafbat UI.
- [smoothmq](https://railway.com/deploy/AJv-64) — A drop-in replacement for AWS SQS
- [Kafka UI](https://railway.com/deploy/kafka-ui) — Kafbat UI — Open-source web UI to monitor and manage Apache Kafka clusters

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