Deploy and Host LLM with vLLM on Railway

Host your own Large Language Model (LLM) instance for scalable, efficient AI infrastructure. Railway automates deployment, resource scaling, and provides easy API access. Just set your model name (must be vLLM-compatible) in your environment variables.

How It Works

1. Set MODEL_NAME to any supported model in your Railway environment variables:

MODEL_NAME="gpt2"

2. The api-gateway listens for requests, enforces authentication with API_KEY, and forwards them to your vLLM backend.
3. OpenAI-compatible APIs let clients and apps connect with zero code changes.

Tip: For production, always set an API_KEY, use larger resource plans for best performance, and consult the vLLM Supported Models doc for compatible model names.

API Gateway Template

api-gateway is an included starter template that acts as your secure front API layer. Users interact through this gateway using an API_KEY for authentication. The gateway forwards requests to your vLLM backend and helps enforce security and rate limiting in production.

API Gateway Template

api-gateway is an included starter template that acts as your secure front API layer. Users interact through this gateway using an API_KEY for authentication. The gateway forwards requests to your vLLM backend and helps enforce security and rate limiting in production.

About Hosting something-oss

Railway makes it easy to run inference servers, caching layers (like Redis), and API gateways with minimal setup. Persistent storage and private networking work out-of-the-box, so your AI stack can scale automatically and is always accessible via OpenAI-compatible API paths.

Common Use Cases

• Deploy private, secure AI chatbots or assistants
• Host scalable machine learning APIs with batching and caching
• Integrate OpenAI-compatible APIs into full-stack apps

Dependencies

• vLLM for model serving and OpenAI-compatible routing
• Redis for fast caching
• 5–30GB volume storage (depending on model size)
• 8–30GB RAM (depending on model size)
• 8–30 vCPUs (for higher throughput and concurrency)

Pricing & Capacity

Resource usage drives hosting costs:

• Small models (up to 2B parameters): modest CPU and RAM; lower monthly price
• Large models (GPT-OSS, Llama 20B+): higher RAM and CPU needed; expect higher costs via Railway’s dashboard

Why Deploy 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 GPT-vLLM 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