Deploy and Host AnythingLLM on Railway

AnythingLLM is an open-source, privacy-first AI workspace by Mintplex Labs that lets you chat with your documents, build agent workflows, and run large language models locally or in the cloud. It supports OpenAI, Anthropic, Ollama, and more—making it one of the most versatile LLM apps to self-host or deploy in minutes.

With this template, you can deploy AnythingLLM to Railway in a single click— no manual Docker setup or infrastructure provisioning required.

About Hosting AnythingLLM

Hosting AnythingLLM on Railway means deploying a fully managed instance of the AnythingLLM application stack—complete with web UI, vector storage, and LLM connectivity—without managing servers or Docker manually. Railway handles the infrastructure, persistent storage, networking, and environment variables automatically. In less than five minutes, you can launch a private, cloud-based RAG (Retrieval-Augmented Generation) workspace for chatting with PDFs, DOCX files, CSVs, or entire knowledge bases. It’s ideal for developers, data teams, or businesses looking for fast, secure, and scalable AI document chat hosting.

Common Use Cases

• Chat with private documents or internal wikis using OpenAI, Claude, or local models
• Prototype enterprise RAG systems with minimal setup (no manual vector DB needed)
• Host agent flows that connect APIs, web scrapers, and file I/O for task automation
• Deploy lightweight LLM workspaces for client demos or AI product MVPs

Dependencies for AnythingLLM Hosting

• Dockerized AnythingLLM Image: mintplexlabs/anythingllm:latest
• Persistent Volume: Mounted at /app/server/storage for workspace and document retention
• API Keys: (optional) for LLM providers such as OpenAI, Anthropic, or Azure OpenAI

Deployment Dependencies

• AnythingLLM Official Documentation
• GitHub Repository
• Railway Documentation

How to Use AnythingLLM After Deployment

1. Once deployed, open your Railway-generated URL (e.g., https://anythingllm-production.up.railway.app).
2. Configure your preferred LLM provider and embedder (OpenAI, Ollama, etc.).
3. Create a workspace and upload documents (PDFs, CSVs, DOCX).
4. Start chatting—AnythingLLM automatically retrieves relevant document chunks for each query.
5. Use the “Agent Flows” tab to build automation pipelines visually.

What Is AnythingLLM?

AnythingLLM is an all-in-one AI chat and agent platform. It integrates local or hosted large language models, retrieval-augmented generation (RAG), and no-code automation (“Agent Flows”). You can deploy it locally, in Docker, or on cloud platforms like Railway. The system is designed for privacy and flexibility—your data stays in your environment.

What Does It Cost to Use AnythingLLM?

• AnythingLLM Software: Free (open source under MIT License).
• Hosting on Railway:
• Free tier available for light testing (limited RAM/runtime).
• Paid plans (~$5–$20/month) for persistent hosting and more memory.
• Model Usage: Costs depend on your selected LLM provider (e.g., OpenAI token usage).

AnythingLLM Alternatives

FAQs

1. Is AnythingLLM free to use? Yes. It’s open-source and free to self-host. You only pay for hosting or API usage from your chosen LLM provider. 2. Can I run AnythingLLM without a GPU? Absolutely. It runs fine on CPU if you’re connecting to cloud LLMs. GPUs are only needed for local inference. 3. What kinds of files can I upload? PDF, DOCX, CSV, Markdown, and text files. AnythingLLM automatically embeds them for semantic search. 4. Can multiple users access one instance? Yes, the self-hosted version (like this Railway deployment) supports multi-user mode via shared workspace access. 5. How secure is my data? Data stays within your Railway container and volume. Nothing is transmitted externally unless your chosen LLM provider is cloud-based.

Implementation Details

Example environment variables you can pre-configure in the template (required ones are already preconfigured for you):

JWT_SECRET=supersecret123
STORAGE_DIR=/app/server/storage
LLM_PROVIDER=openai
OPEN_AI_KEY=your_openai_key_here
EMBEDDING_ENGINE=openai
VECTOR_DB=lancedb
PORT=3001

System Requirements

AnythingLLM is designed to be lightweight and flexible — you can run it on anything from a small Railway instance to a full GPU server.
Your actual requirements depend on the type of LLM, embedder, and vector database you connect to.

Recommended Configuration

These are the minimum recommended values for running AnythingLLM smoothly on Railway.
They’re sufficient for uploading documents, chatting, and using core features.

> Tip: Attach a persistent volume in Railway to retain uploaded files and embeddings across deployments.

LLM Provider Impact

Your chosen LLM provider affects performance and resource usage:

• Cloud models (OpenAI, Anthropic, Azure OpenAI):
  Provide high-quality responses with near-zero local resource load, but require valid API keys.
• Local models (Ollama, LM Studio, etc.):
  These require GPU or CPU power and should ideally be hosted on another machine.
  AnythingLLM can connect to remote LLM endpoints over API.

> Tip: If your Railway instance has no GPU, host Ollama or another model on a separate machine and connect via API.

Embedder Selection Impact

Embedders generate text embeddings for semantic search.
They can be local or external — and external ones impose no local overhead.

• Cloud Embedders (OpenAI, Hugging Face): Fast and efficient; use API keys.
• Local Embedders: Require more CPU/GPU resources but allow full offline operation.

> Tip: You can host your embedder remotely on a GPU-equipped device and connect it to AnythingLLM via API.

Vector Database Impact

Vector databases store document embeddings for retrieval-augmented generation (RAG).
All supported vector DBs in AnythingLLM are lightweight and scale easily.

• Default: LanceDB — embedded by default, handles large document volumes efficiently.
• External Options: You can connect to scalable databases like Chroma, Pinecone, or Milvus.

> At the recommended specs above, LanceDB can handle millions of document vectors with no issue.

In summary, AnythingLLM runs comfortably on Railway’s 2 GB plan for typical RAG or chat use cases.
If you’re connecting to local or GPU-heavy LLMs, offload the model to another host and use API connections.

Why Deploy AnythingLLM on Railway | 1 Click LLM app hosting 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 AnythingLLM on Railway | 1 Click LLM app hosting 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.