I'm building an internal AI tool and need a simple model hosting setup

A simple, practical setup for internal model hosting is:

Option A: Easiest path — managed API + internal proxy

If you want the fastest setup with minimal ops:

1. Use a hosted model provider.
2. Put an internal API gateway/proxy in front of it.
3. Add auth, logging, rate limits, and prompt filtering internally.

Good for: quick rollout, low maintenance
Tradeoff: data leaves your environment unless the provider offers strong enterprise controls

---

Option B: Simple self-hosted setup

If you want the model to stay inside your environment, use:

1) Model server

Pick one serving layer:

• vLLM — great throughput, popular for open models
• TGI (Text Generation Inference) — solid production option
• Ollama — easiest for small/internal prototypes
• LM Studio server — simple local dev/testing

2) Model choice

Use an open-weight model that fits your hardware:

• Small/cheap: Llama 3.1 8B, Mistral 7B, Qwen2.5 7B
• Better quality, more hardware: Llama 3.1 70B, Qwen2.5 32B

3) Internal API

Wrap the model server with a small service:

• FastAPI / Flask / Node
• Handles:
  • authentication
  • request validation
  • prompt templates
  • rate limiting
  • audit logs

4) Storage and logs

• Store prompts/responses in Postgres or SQLite for prototype
• Use a log stack like ELK / Loki / CloudWatch / Datadog
• Be careful with sensitive data retention

5) Deployment

• Docker Compose for a small internal deployment
• Kubernetes if you need scaling and multiple teams

---

Minimal architecture

Internal App
   -> Auth/API Gateway
   -> AI Service (FastAPI)
   -> Model Server (vLLM/TGI/Ollama)
   -> GPU/CPU host

---

Recommended “simple” stack

If you want something easy and production-friendly:

• vLLM
• FastAPI
• Docker Compose
• Postgres for logs
• Nginx or Traefik as reverse proxy

---

Example deployment pattern

• One GPU server runs the model server
• One small app server runs the API wrapper
• Internal apps call the wrapper over HTTPS
• Only the wrapper can talk to the model server

This keeps the model process isolated and easier to secure.

---

Key considerations

• GPU memory: determines which model size you can run
• Concurrency: vLLM handles multiple users better than a basic inference script
• Security: authenticate internal users and avoid storing sensitive prompts unless needed
• Monitoring: track latency, token usage, and failures
• Fallback: have a smaller backup model or queue when load is high

---

If you want the simplest possible prototype

Use:

• Ollama
• one Docker container
• a small FastAPI wrapper
• basic auth

That gets you a working internal demo in a day.

If you want, I can give you:

1. a minimal Docker Compose setup,
2. a FastAPI wrapper example, or
3. a recommended stack based on your hardware.