Railway

Deploy Qdrant (Vector DB)

Qdrant vector database with persistent storage for AI/RAG

Deploy Qdrant (Vector DB)

Just deployed

/qdrant/storage

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

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:

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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