---
title: "Deploy Memgraph"
description: "[Updated Aug 2026] Production Ready Memgraph Instance"
category: "Storage"
url: https://railway.com/deploy/memgraph
---

# Deploy Memgraph

[Updated Aug 2026] Production Ready Memgraph Instance

**[Deploy Memgraph on Railway](https://railway.com/template/memgraph)**

- **Creator:** Ramadan Omar's Projects
- **Category:** Storage

## Template content

### Memgraph https://memgraph.com/docs/memgraph-logo-navigation.svg

- **Image:** memgraph/memgraph-mage:3.12.0
- **Start command:** `sh -c 'exec /usr/lib/memgraph/memgraph --bolt-address=:: --memory-limit=${MEMGRAPH_MEMORY_LIMIT:-1024} --log-level=${MEMGRAPH_LOG_LEVEL:-WARNING} --also-log-to-stderr ${MEMGRAPH_EXTRA_ARGS:-}'`

## Documentation

# Deploy and Host Memgraph on Railway

Memgraph is an open source, in-memory graph database written in C++. It is fast, ACID
compliant, and runs transactional and analytical workloads in the same engine. It
speaks Cypher and Bolt, so Neo4j drivers work out of the box, and it ships with MAGE,
a library of over 300 graph algorithms.

## About Hosting Memgraph

This template gives you a production-ready Memgraph instance in one click. It runs the
official `memgraph/memgraph-mage` image, pinned to a specific version for stability.
Authentication is on from the start: an admin user is created on first boot with a
randomly generated password. Your data lives on a Railway volume mounted at
`/var/lib/memgraph`, so snapshots and write-ahead logs survive restarts and redeploys.
A public TCP endpoint exposes the Bolt protocol, and ready-made connection strings are
published as `BOLT_URL` and `BOLT_PRIVATE_URL`. Memory limit and log level are plain
environment variables, no start-command editing needed. Plan at least 2 GB of RAM,
since Memgraph keeps its working set in memory.

## Common Use Cases

- **GraphRAG and AI agents**: ground LLM answers in a knowledge graph, with built-in
  vector search and multi-hop traversals. Works with LangChain and LlamaIndex.
- **Real-time recommendations**: run collaborative filtering and link prediction on
  live data, with triggers that fire as soon as new data arrives.
- **Fraud detection**: find suspicious patterns with multi-hop relationship queries
  that relational databases struggle with.
- **Network and dependency analysis**: model infrastructure, supply chains, or social
  graphs and run centrality and community algorithms on them.

## Dependencies for Memgraph Hosting

- None. This is a one-click deploy with production-ready configuration out of the
  box: pinned official image, generated admin password, persistent volume, public
  Bolt endpoint, and sane memory settings.

### Deployment Dependencies

The template itself depends on nothing. These are the docs, SDKs, and tools you will
actually use once it is running:

- [Memgraph documentation](https://memgraph.com/docs)
- [MAGE graph algorithm library](https://memgraph.com/docs/advanced-algorithms)
- [Client SDKs (Python, JavaScript, Go, Java, ...)](https://memgraph.com/docs/client-libraries)
- [Memgraph Lab](https://memgraph.com/docs/data-visualization), an optional visual UI
  you can run locally to query and explore your graph

### Implementation Details

Connect with any Neo4j-compatible Bolt driver using the `BOLT_URL` and
`MEMGRAPH_PASSWORD` variables from the service:

```python
from neo4j import GraphDatabase

driver = GraphDatabase.driver(
    "bolt://your-proxy-domain:port",          # BOLT_URL
    auth=("memgraph", "your-password"),       # MEMGRAPH_USER / MEMGRAPH_PASSWORD
)
with driver.session() as session:
    print(session.run("RETURN 'hello graph' AS msg").single()["msg"])
```

Good to know:

- `MEMGRAPH_USER` and `MEMGRAPH_PASSWORD` only apply on the first boot. To change the
  password later, run `SET PASSWORD TO 'new'` in Cypher. Changing the variable alone
  won't do it.
- Your data stays in the attached volume across restarts, redeploys, and image
  upgrades.
- If you only need the C++ algorithms, remove the `PYTHONPATH` variable to skip
  MAGE's Python modules and save about 700 MB of RAM.
- Want a visual UI? Run Memgraph Lab locally with
  `docker run -p 3000:3000 memgraph/lab` and point it at your `BOLT_URL`.

## FAQ

### How do I know this template will be maintained?

It is built and maintained by [Webified](https://webified.eu), a software agency. We
use this exact setup in commercial projects for paying clients, so we have a direct
interest in keeping it healthy. Versions are pinned, and we review new Memgraph
releases before bumping them. If you run into anything, ask in the template's support
thread on Railway.

### What happens to my data on restarts, redeploys, or upgrades?

Nothing. Snapshots and write-ahead logs live on the attached Railway volume, which
survives all of those. Upgrading means changing the image tag and redeploying. Your
data stays where it is.

## Why Deploy Memgraph 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 Memgraph 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.


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