Docker for On-Premise AI: From 3-Day Deploys to 15 Minutes
The first on-premise deploy of RAG Enterprise PRO was a disaster: 3 days of work, Python version conflicts, incompatible CUDA drivers, and a furious sysadmin. Then we containerized everything. Today we deploy in 15 flat minutes.
The First On-Premise Deploy Disaster
We'll tell you about our first on-premise deploy because we believe mistakes teach more than successes. The client was a Milan law firm with 25,000 confidential documents. Zero cloud — all data had to stay on their physical server. Day 1: we arrive with our dependency list. Python 3.11, PyTorch 2.1, CUDA 12.1, plus twenty-something Python libraries. The client's server had Ubuntu 20.04 with system Python 3.8. Upgrading Python broke their internal monitoring tool. The sysadmin was not happy. Day 2: Python mess resolved, we hit NVIDIA driver issues. The server had driver 510, we needed 535 for CUDA 12.1. The driver upgrade required rebooting a server hosting other services. Day 3: everything installed, but sentence-transformers wanted a specific numpy version conflicting with another client library. In the end it worked, but the process was a nightmare.
Docker Compose: One File, Entire System
After that experience, we containerized every component. Our docker-compose.yml orchestrates 5 services: the FastAPI backend, the React frontend (served by nginx), Qdrant for the vector database, Ollama for local LLM inference, and a dedicated embedding service. The beauty is the client only needs Docker installed and NVIDIA drivers. That's it. We don't touch their Python, we don't touch their system libraries, we don't risk breaking anything else. Deploy today works like this: we copy the docker-compose.yml and the .env config file, run "docker compose up -d", and in 15 minutes the system is operational. AI models are automatically downloaded on first startup. The second deploy — an accounting firm in Rome — went smooth as silk. The sysadmin asked us "that's it?".
GPU Passthrough and Container Security
The most technical challenge was getting NVIDIA GPUs working inside Docker containers. With the NVIDIA Container Toolkit, containers access the host GPU directly with no measurable overhead. We benchmarked: the difference between bare-metal and containerized inference is 0.3%. Our clients choose on-premise for one reason: data must not leave the corporate perimeter. Docker helps us guarantee this. Each container has its own internal network. We configure Docker firewall to block all outbound traffic. The only exposed port is 443 for the frontend's HTTPS. Backups are simple: a cron job running docker exec on Qdrant to export snapshots, and rsync of Docker volumes to a local NAS. The client has total control of their data, their keys, and their backups. No vendor lock-in.
FAQ About Docker Deployment
Q: Do you need advanced Docker skills to manage RAG Enterprise PRO? A: No. We provide management scripts that wrap Docker commands. The client uses simple commands like "rag-start", "rag-stop", "rag-backup". Q: How much hardware is needed to run everything with Docker? A: Minimum: 16 GB RAM, 4 CPU cores, GPU with 8 GB VRAM (like RTX 3060), 100 GB SSD. With this setup we handle up to 30,000 documents and 20 concurrent users. Q: Does Docker introduce performance overhead vs native installation? A: Negligible. 0.3% overhead on GPU inference and 1-2% on network I/O. In return you get total isolation, reproducible deploys and zero-downtime updates.
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