How to Build a Self-Hosted AI Gateway With LiteLLM and Open WebUI

To build a self-hosted AI gateway using LiteLLM and Open WebUI to simplify management of multiple AI providers. By placing LiteLLM as a single gateway between user interfaces and various AI services (OpenAI, Anthropic, Groq, Ollama), the system allows providers to be swapped or added without rewriting application code. The open-source implementation can be deployed in under 30 minutes using Docker Compose, with configuration files for routing, authentication, and provider credentials.

If you've ever self-hosted AI tools, you know how quickly things get messy. One app talks to OpenAI. Another uses Anthropic. You spin up Ollama locally and now there's a third endpoint to manage. Authentication is different everywhere. Switching models means rewriting integration code. And before long, you're spending more time maintaining glue code than actually building anything. I ran into this exact problem — so I built a cleaner setup. The idea is simple: put a single gateway in front of every provider, so the rest of your stack only ever talks to one API. I open-sourced the full working implementation here: Clone it. Run docker compose up . You'll have a working AI gateway in under 30 minutes. What This Stack Does - One API → OpenAI, Anthropic, Groq, and Ollama all behind a single OpenAI-compatible endpoint - One frontend → Open WebUI as the unified chat interface - Secure remote access → Cloudflare Tunnel, no exposed ports, no open firewall rules - Easy to maintain → providers can change underneath without touching your apps The full setup takes roughly 20–45 minutes depending on Docker image downloads and whether you already have local Ollama models installed. The Stack Nothing exotic here — just well-composed tools: | Component | Role | |---|---| LiteLLM | Gateway / routing layer | Open WebUI | Chat frontend | PostgreSQL | State + metadata | Docker Compose | Orchestration | Cloudflare Tunnel | Secure remote exposure | Architecture User ↓ Open WebUI ↓ LiteLLM gateway ↓ OpenAI / Anthropic / Groq / Ollama The key insight: your apps and frontend only talk to LiteLLM. Providers become interchangeable underneath. Add a new model, swap a provider, change routing — nothing else needs to know. Who This Is For - Developers experimenting with local AI infrastructure - Teams consolidating multiple providers behind one API layer - Engineers building internal AI tooling - Anyone tired of maintaining separate provider integrations If you've ever thought "there has to be a simpler way to manage all these AI endpoints" — this is that simpler way. Why This Stack Exists Most self-hosted AI environments become hard to manage surprisingly fast. One application talks directly to OpenAI. Another uses Anthropic separately. Local Ollama models need their own endpoints. Authentication is inconsistent, and model switching slowly turns into infrastructure sprawl. By placing LiteLLM in front of every provider, the rest of your system only needs to understand one interface. Providers can change, local models can be added, routing logic can evolve — without rewriting frontend or application logic every time. Prerequisites Before starting containers, make sure you have: - Docker Desktop - Docker Compose curl cloudflared - Ollama optional, for local models Quick verification: docker --version docker compose version cloudflared --version curl --version If using local Ollama models: ollama list If installed models appear, local inference is ready. Repository Structure The repo is intentionally lightweight: ├── Docker-compose.yml ├── litellm-config.yml └── .env Each file has a distinct job: Docker Compose orchestrates services, LiteLLM config handles routing and model aliases, .env stores secrets and runtime configuration. Setting Up Environment Variables Your .env file is where provider credentials live. Create or update it in the project root: LITELLM MASTER KEY=sk-very-strong-key OPENAI API KEY=... ANTHROPIC API KEY=... GROQ API KEY=... OLLAMA CLOUD API BASE=https://<host /v1 OLLAMA CLOUD API KEY=... A few things that matter more than people expect: - LITELLM MASTER KEY becomes the authentication layer between Open WebUI and LiteLLM - These values should never be committed into Git - Weak keys become a real security problem once remote access exists Even if this starts as a personal setup, treat the environment config like production from day one. Wiring Docker Compose The goal here is making sure the containers can talk to each other. Most deployment failures come from small config mismatches rather than Docker itself. Your Docker-compose.yml should point Open WebUI directly at LiteLLM: OPENAI API BASE URL=http://litellm:4000/v1 OPENAI API KEY=${LITELLM MASTER KEY} This tells Open WebUI where the gateway lives and which key to use. LiteLLM should mount the configuration file correctly: ./litellm-config.yml:/app/config.yaml ⚠️ That filename matters more than it looks. A typo here can cause Docker to create a directory instead of mounting the file — which leads to confusing startup errors later. Configuring LiteLLM Inside litellm-config.yml , LiteLLM defines model aliases, provider routing, gateway behavior, and authentication settings. The most important section is the master key: general settings: master key: os.environ/LITELLM MASTER KEY This keeps secrets outside the YAML file and makes credential rotation easier later. Inside model list , make sure provider model IDs are current. Model names change more frequently than most people expect — especially across Groq and newer OpenAI releases. Starting the Stack Once your config looks right, start everything: docker compose up -d --force-recreate The initial startup may take a minute while Docker pulls images, initializes PostgreSQL, and creates container state. Verify container health: docker ps --format 'table {{.Names}}\t{{.Status}}\t{{.Ports}}' You should see open-webui , litellm , and litellm-db all running. If a container exits immediately, check its logs before moving forward: docker logs <container-name Validating the Gateway Before touching Open WebUI, validate LiteLLM first. The /v1/models endpoint confirms authentication works, providers loaded correctly, and model routing initialized. set -a && source .env && \ curl -s http://localhost:4000/v1/models \ -H "Authorization: Bearer $LITELLM MASTER KEY" For readable output: set -a && source .env && \ curl -s http://localhost:4000/v1/models \ -H "Authorization: Bearer $LITELLM MASTER KEY" \ | python3 -m json.tool | head -n 80 If this endpoint fails, Open WebUI will almost certainly fail too — so resolve gateway issues first. Verifying Open WebUI Once LiteLLM responds correctly, open the interface: http://localhost:3000 You should be able to create chats, select models, and send prompts normally. If the model dropdown is empty, LiteLLM authentication is usually the cause — mismatched master keys, stale model IDs, or invalid provider credentials. Keeping Provider Models Current This catches a lot of people off guard: provider model identifiers change more often than you'd think. A deployment that worked perfectly a few months ago can break because a provider deprecated a model name. Checking Local Ollama Models ollama list Checking Groq Models set -a && source .env && \ curl -s https://api.groq.com/openai/v1/models \ -H "Authorization: Bearer $GROQ API KEY" \ -H "Content-Type: application/json" After updating model IDs in litellm-config.yml , recreate the stack: docker compose up -d --force-recreate Secure Remote Access with Cloudflare Tunnel At this point, the stack only exists locally. The next step is exposing Open WebUI to the internet — without opening inbound ports, exposing your home IP, or managing reverse proxies manually. Cloudflare Tunnel creates an outbound encrypted connection from your machine to Cloudflare's edge. You get: - Automatic HTTPS - Hidden origin infrastructure - Cloudflare proxy protection - Simple DNS management Move DNS to Cloudflare - Add your domain to Cloudflare - Update nameservers at your registrar - Wait for propagation Authenticate cloudflared cloudflared tunnel login This opens a browser window for authorization. Create the Tunnel cloudflared tunnel create openwebui Cloudflare generates a tunnel UUID and a credentials JSON file. Route a Subdomain Assuming your domain is chat.yourdomain.tech : cloudflared tunnel route dns openwebui chat.yourdomain.tech Create the Tunnel Configuration Create ~/.cloudflared/config.yml : tunnel: openwebui credentials-file: ~/.cloudflared/<tunnel-uuid .json ingress: - hostname: chat.yourdomain.tech service: http://localhost:3000 - service: http status:404 Replace <tunnel-uuid with the generated filename. Run the Tunnel cloudflared tunnel run openwebui For persistent startup on macOS: cloudflared service install cloudflared service start Running the tunnel as a background service is significantly more reliable than keeping it in a terminal session. Verifying Remote Access Quick DNS and connectivity check: dig +short chat.yourdomain.tech curl -I https://chat.yourdomain.tech Always use HTTPS for remote access. Cloudflare Tunnel is designed around secure proxied traffic. Operational Health Checks Once the stack is stable, this single command gives you a quick overview of everything: set -a && source .env && \ echo "== Docker services ==" && \ docker ps --format 'table {{.Names}}\t{{.Status}}\t{{.Ports}}' && \ echo "\n== Local Ollama models ==" && \ ollama list && \ echo "\n== Groq model count ==" && \ curl -s https://api.groq.com/openai/v1/models \ -H "Authorization: Bearer $GROQ API KEY" \ -H "Content-Type: application/json" \ | python3 -c 'import sys,json; d=json.load sys.stdin ; print len d.get "data", ' && \ echo "\n== LiteLLM models ==" && \ curl -s http://localhost:4000/v1/models \ -H "Authorization: Bearer $LITELLM MASTER KEY" Even for personal deployments, having this kind of visibility saves a lot of debugging time. Troubleshooting Stable deployments drift. Provider APIs change, Docker mounts break, credentials expire, model IDs get deprecated. Here are the most common failure patterns. Open WebUI Loads but Models Are Missing Empty dropdowns, missing providers, or authentication errors in LiteLLM logs. docker logs --tail 200 litellm Verify model visibility directly: set -a && source .env && \ curl -s http://localhost:4000/v1/models \ -H "Authorization: Bearer $LITELLM MASTER KEY" Typical fixes: - Verify OPENAI API KEY=${LITELLM MASTER KEY} - Confirm master key uses the environment variable - Recreate containers: docker compose up -d --force-recreate docker compose restart open-webui LiteLLM Fails with IsADirectoryError Docker accidentally created a directory instead of mounting the YAML file. ls -la ./litellm-config.yml ./litellm-config.yaml grep -n "litellm-config" Docker-compose.yml Correct mount: ./litellm-config.yml:/app/config.yaml Then recreate: docker compose up -d --force-recreate litellm Works Locally but Not Through Cloudflare If local access works but the public hostname fails, focus on the tunnel: cloudflared tunnel list cloudflared tunnel info openwebui cat ~/.cloudflared/config.yml dig +short chat.yourdomain.tech curl -I https://chat.yourdomain.tech Most remote-access failures come from inactive tunnel connectors, incorrect ingress targets, missing proxied DNS records, or running cloudflared in a temporary terminal session. Models Appear but Generation Fails If /v1/models works but prompts fail — provider credentials may be invalid, quotas exhausted, or model IDs no longer exist. set -a && source .env && \ env | grep -E '^ OPENAI API KEY|GROQ API KEY|ANTHROPIC API KEY|LITELLM MASTER KEY =' \ | sed 's/=. /=<set /' Then inspect LiteLLM logs: docker logs --tail 300 litellm Refreshing provider model IDs solves this surprisingly often. Security Recommendations Once remote access exists, basic hardening matters: - Use a strong LITELLM MASTER KEY - Don't expose LiteLLM directly to the internet - Rotate provider API keys periodically - Keep CORS rules restrictive For private or team usage, Cloudflare Access adds identity-aware access control in front of Open WebUI — worth enabling. Capture a Known-Good Baseline Once things are stable, save: - docker ps output - /v1/models output - Active model aliases - Tunnel status: cloudflared tunnel info openwebui When something breaks later, comparing against a working snapshot is almost always faster than debugging from scratch. Try It Yourself The full working implementation — Docker Compose, LiteLLM config, environment setup — is all here: Clone it, add your API keys, run docker compose up , and you'll have a working AI gateway. If you found this useful, ⭐ the repo — it helps more people find it. Got questions or improvements? Open an issue or drop a comment below. I'm actively maintaining this. Originally published on HackerNoon.