---
title: "Deploy Qdrant (VectorDB)"
description: "Qdrant vector database with persistent storage for AI/RAG"
category: "Other"
url: https://railway.com/deploy/qdrant-vectordb
---

# Deploy Qdrant (VectorDB)

Qdrant vector database with persistent storage for AI/RAG

**[Deploy Qdrant (VectorDB) on Railway](https://railway.com/template/qdrant-vectordb)**

- **Creator:** Protemplate
- **Category:** Other
- **Total deploys:** 2

## Template content

### Qdrant https://raw.githubusercontent.com/qdrant/qdrant/master/docs/logo.svg

- **Image:** qdrant/qdrant:latest
- **Health check:** /healthz
- **Public domain:** Yes

## Documentation

# Deploy and Host Qdrant on Railway

Qdrant is an open-source, high-performance vector search engine and database written in Rust. It stores embeddings with their JSON payloads and serves fast, filterable similarity search over REST and gRPC APIs, making it the retrieval backbone for RAG pipelines, semantic search, recommendations, and AI agent memory.

## About Hosting Qdrant

Hosting Qdrant means running a stateful database that keeps your vector collections, HNSW indexes, and payloads on persistent disk. Self-managing it normally involves provisioning storage, exposing the HTTP and dashboard port, enabling API key authentication, and tuning memory for your embedding dimensions and collection size. This template wraps the official `qdrant/qdrant` Docker image so it runs cleanly on Railway: it binds Qdrant's HTTP service to the platform port, mounts a persistent volume at `/qdrant/storage` so data survives redeploys and restarts, exposes the `/healthz` endpoint for health checks, and auto-generates an optional API key. Qdrant is self-contained, so no external database or cache is required.

## Common Use Cases

- **RAG pipelines** — store document embeddings and retrieve relevant chunks to ground LLM responses
- **Semantic search** — natural-language search across product catalogs, documentation, and knowledge bases
- **Recommendation systems** — surface similar items, content, or users via vector similarity
- **AI agent memory** — persist conversation context and tool results as searchable vectors
- **Anomaly and duplicate detection** — find near-neighbors to flag outliers or matching records

## Dependencies for Qdrant Hosting

- **Persistent volume** — stores collections, indexes, and payloads at `/qdrant/storage` (provisioned by this template)
- **No external database required** — Qdrant is fully self-contained
- **Embedding model (your app side)** — generate vectors with OpenAI, Cohere, or a local model before upserting

### Deployment Dependencies

- [Qdrant documentation](https://qdrant.tech/documentation/)
- [Qdrant Docker image](https://hub.docker.com/r/qdrant/qdrant)
- [Qdrant GitHub repository](https://github.com/qdrant/qdrant)
- [REST API reference](https://api.qdrant.tech/)

### Implementation Details

Qdrant binds to `0.0.0.0` and serves its REST API and web dashboard on port `6333`. This template sets `QDRANT__SERVICE__HTTP_PORT` to match the Railway domain target port and persists data via the mounted volume:

```env
QDRANT__SERVICE__API_KEY=${{secret(32)}}
QDRANT__SERVICE__HTTP_PORT=6333
QDRANT__STORAGE__STORAGE_PATH=/qdrant/storage
```

After deploy, generate a public domain to reach the REST API and dashboard, or connect from another Railway service over private networking with `http://${{Qdrant.RAILWAY_PRIVATE_DOMAIN}}:${{Qdrant.QDRANT__SERVICE__HTTP_PORT}}`. When an API key is set, send it on every request via the `api-key` header.

## Why Deploy Qdrant 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 Qdrant on Railway, you get a pre-configured vector database with a persistent volume, automatic health checks, managed SSL, and private networking to your application services. Host your servers, databases, AI agents, and more on Railway.


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