Build You Own Enterprise LLM Platform

Enterprise LLM hosting in 2025 is about building a fast, secure, and compliant home for your language models instead of relying only on public APIs.

This guide will show you how an enterprise can design the architecture and then deploy a practical stack using Docker, vLLM‑style serving, and an Nginx reverse proxy on GPU servers from a provider like PerLod Hosting.

In this setup, the LLM becomes a core internal service, much like your databases or identity systems. You manage access, logging, and change control, treating every new model version as a production deployment, not just a lab experiment.

What is Enterprise LLM Hosting?

Enterprise LLM hosting means running large language models in a controlled environment where you set the rules for security, performance, and compliance.

Instead of sending all prompts to public cloud APIs, you host the models yourself on GPU servers or private cloud so you can protect data, tune latency, and control costs.

Why Enterprises Need Private LLM Hosting?

Public LLM APIs are fast to start with, but they do not always meet enterprise needs for privacy, legal control, and predictable performance. Many industries now prefer private or hybrid LLM hosting because they want to:

• Keep sensitive data inside trusted environments.
• Meet data residency and data sovereignty rules.
• Optimize cost and latency.

By building your own LLM platforms, companies get better visibility into how AI is used, what data it sees, and how it behaves over time.

Here is a quick comparison between public vs private LLMs:

Aspect	Public LLM APIs	Private or Enterprise LLM Hosting
Data control	Provider controls storage and processing.	Your team controls storage, processing, and retention.
Compliance	General controls, not always sector‑specific.	It can be tailored to your regulations and regions.
Latency	Depends on the provider region and load.	It can be tuned with location and hardware choices.
Customization	Limited model changes and tools.	Full control over models, prompts, tools, and RAG.
Cost structure	Per‑token or per‑call pricing.	Infrastructure‑driven, optimized via hardware and scale.

Public LLMs are useful for some workloads, but private hosting gives enterprises the flexibility and control needed for sensitive workloads.

Which Hosting Models Are Best for Enterprises?

Enterprises generally choose one of these three models including on-premises, private cloud, or dedicated hosting, and a hybrid setup.

1. On‑premises: Your LLMs run in your own data center or server rooms with your hardware.

This option provides maximum physical control and can simplify strict legal and regulatory requirements, especially for finance, the public sector, or healthcare.

Also, you must:

• Handle power, cooling, and hardware refresh cycles.
• Design and operate your own network and security stack.

2. Private cloud or dedicated hosting: In this method, you use dedicated GPU servers or AI Hosting in a provider’s data centers, often inside private networks and virtual private clouds.

This model gives you:

• Dedicated hardware with strong isolation.
• Easier scaling and faster provisioning.
• Better global region choice.

Tip: PerLod provides GPU dedicated servers and AI Hosting nodes in multiple regions with private networking, so you can run your own LLM stack without owning physical data centers.

3. Hybrid architecture: In a hybrid setup, you keep your sensitive jobs on your own servers or in a fully trusted region, and less sensitive and more flexible jobs run in other regions or cloud platforms.

Hybrid designs are common for large enterprises that already have data centers but want the flexibility of external GPU clusters when needed.​

Core Architecture for Enterprise-grade LLM Hosting

Most modern enterprise LLM platforms share a similar layered architecture. Here is the core architecture for enterprise LLM hosting:

1. Inference and model layer: This layer runs the LLM engines on GPU nodes. Typical choices include:

• High‑throughput engines like vLLM or similar, which expose an OpenAI‑compatible API over HTTP.​
• GPUs are sized according to the model and context length.

These services are usually containerized and run under Docker or Kubernetes to make deployment repeatable.

2. API and gateway layer: The gateway is the single entry point for all external and internal LLM calls. It does these things:

• Terminates TLS.
• Handles authentication.
• Enforces rate limiting and request size limits.
• Routes traffic to specific models or model versions.

Nginx, Envoy, and dedicated API gateways are common tools for this layer.

3. Data and RAG layer: Many enterprise use cases rely on RAG systems, where the LLM uses internal documents and data. This layer includes:

• Vector databases and embeddings storage.
• ETL or indexing pipelines that keep documents in sync.
• Connectors to internal systems.

4. Security and governance: Security is built into every layer, and key elements are:

• Identity and access management and role‑based access control.
• Secrets management for API keys, tokens, and database credentials.
• Logging and audit trails for all LLM calls and admin actions.

5. Observability and MLOps: Finally, the observability layer ensures reliability and continuous improvement. It tracks:​

• Latency, errors, and tokens per second.
• Model versions, rollout history, and rollback plans.
• Usage patterns and cost per feature or team.

PerLod provides the bare metal or dedicated GPU layer on which you build these logical layers, using your preferred stack for containers, gateways, and observability.

Hardware and Performance Planning for Enterprise LLM Platform

Performance starts with the right hardware and a clear estimate of traffic. By thinking about model size, context length, concurrency, and data flow across the network, you can choose hardware that delivers strong throughput and low latency without wasting budget.

GPU and VRAM: Larger models and longer context windows need more GPU memory. When you want to plan, think about these things:

• Model size (parameters).
• Context length (max tokens per prompt and response).
• Concurrency (number of parallel requests).

For cost and latency, many enterprises choose optimized or quantized models that balance quality with resource usage.

CPU, RAM, and storage: GPU nodes still need strong CPU and RAM to feed data to the GPUs without bottlenecks. It is recommended to use:

• Enough CPU cores to handle tokenization and pre‑processing.
• Fast NVMe SSDs for model loading, caching, and vector search.
• RAM sized to handle batching and multiple processes.

Network: Network design affects both latency and security. Best practices include:​

• Private subnets for LLM nodes and vector stores.
• Low‑latency links between LLM nodes, RAG services, and gateways.
• VPN or private links from corporate networks to your AI cluster.

Example Setup: Deploy a Secure LLM API

At this step, we want to deploy a secure and OpenAI-compatible LLM API on a GPU server from PerLod using Docker, vLLM, and Nginx.

The first step is to prepare your GPU server:

• Provision a GPU dedicated server or AI Hosting node on PerLod with an NVIDIA GPU that matches your chosen model’s VRAM needs.
• Install Ubuntu 22.04+ and ensure NVIDIA drivers are installed, and nvidia-smi works.

Confirm that the GPU is visible:

nvidia-smi

You should see the GPU model and driver version.

Update your system packages with the command below:

sudo apt update && sudo apt upgrade -y

Install Docker from the official Docker repository and add your user to the Docker group so you can run containers easily.

Install required tools:

sudo apt install ca-certificates curl gnupg -y

Add the Docker GPG key and repository:

sudo install -m 0755 -d /etc/apt/keyrings
curl -fsSL https://download.docker.com/linux/ubuntu/gpg \
| sudo gpg --dearmor -o /etc/apt/keyrings/docker.gpg

echo \
"deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.gpg] \
https://download.docker.com/linux/ubuntu \
$(lsb_release -cs) stable" \
| sudo tee /etc/apt/sources.list.d/docker.list > /dev/null

Install Docker:

sudo apt update
sudo apt install docker-ce docker-ce-cli containerd.io -y

Add your user to the Docker group so you can run containers without sudo:

sudo usermod -aG docker $USER

Log out and log back in to apply the changes.​

Then, pull the official vLLM OpenAI‑style image with the command below:

docker pull vllm/vllm-openai:latest

Start a test container with a small model by using the command below:

docker run --gpus all --rm \
-p 8000:8000 \
-e VLLM_MODEL="Qwen/Qwen2.5-0.5B" \
--name vllm-test \
vllm/vllm-openai:latest

In another SSH session on the same server, send a test request:

curl http://127.0.0.1:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Qwen/Qwen2.5-0.5B",
    "messages": [{"role": "user", "content": "Say hello in one short sentence."}]
  }'

You should get a valid JSON response; this proves the engine works on your GPU node.

Note: For production, run the container detached with “–restart unless-stopped” and upgrade to a suitable model like a 7B variant, depending on GPU VRAM.

docker run --gpus all -d \
  -p 8000:8000 \
  -e VLLM_MODEL="Qwen/Qwen2.5-7B" \
  --restart unless-stopped \
  --name vllm-prod \
  vllm/vllm-openai:latest

Check container status and logs:

docker ps
docker logs -f vllm-prod

Now we want to add Nginx as a secure reverse proxy. Install Nginx with the command below:

sudo apt update
sudo apt install nginx -y

Point your domain to the GPU server’s IP. Then, use Certbot to get a TLS certificate and configure HTTPS:

sudo apt install certbot python3-certbot-nginx -y
sudo certbot --nginx -d llm.yourcompany.com

This sets up HTTPS and redirects HTTP to HTTPS automatically.

At this point, you must protect the API with an API key. Generate an API key with:

openssl rand -hex 32

In your Nginx site config file for your domain, add a location /v1/ block that:

• Checks the Authorization header for a Bearer token matching your key.
• Proxy requests to “http://127.0.0.1:8000“, where vLLM is listening.

sudo nano /etc/nginx/sites-available/llm.yourcompany.com

Inside the server { … } block that listens on port 443, add a location /v1/ block like this, replace the key with your real one:

location /v1/ {
    # Simple API key check on Authorization header
    if ($http_authorization !~* "Bearer 2f91f4c2c9f44d5fa86b7f0f0e9d98afad3e6f3ee20f0cfc3dd8f6dbe66c999") {
        return 401;
    }

    proxy_pass http://127.0.0.1:8000;

    proxy_set_header Host $host;
    proxy_set_header X-Real-IP $remote_addr;
    proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
    proxy_set_header X-Forwarded-Proto $scheme;

    proxy_connect_timeout 60s;
    proxy_send_timeout 300s;
    proxy_read_timeout 300s;
}

Once you’re done, reload Nginx:

sudo nginx -t
sudo systemctl reload nginx

From your laptop or a trusted client, send a test call through HTTPS:

curl https://llm.yourcompany.com/v1/chat/completions \
  -H "Authorization: Bearer 2f91f4c2c9f44d5fa86b7f0f0e9d98afad3e6f3ee20f0cfc3dd8f6dbe66c999" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Qwen/Qwen2.5-7B",
    "messages": [{"role": "user", "content": "Explain enterprise LLM hosting in one short paragraph."}]
  }'

Now you have a secure LLM endpoint running on your own GPU server.

Tip: If you want a full, step‑by‑step tutorial focused on building an OpenAI‑compatible API server, you can follow this guide on Setting up an OpenAI-compatible API Server.

To harden the OS and network:

• Use UFW to allow only SSH and HTTPS, keeping the LLM port private.
• Disable password SSH login and use SSH keys, and restrict SSH to your office IPs.
• Keep the OS and Docker images patched and scan images regularly.​

For fully hardened GPU servers, you can check this guide on Best Practices for GPU Hosting Environments Security.

FAQs

Conclusion

Enterprise LLM hosting is a core part of how modern companies use AI, especially when data protection and control matter. By combining proper architecture, strong security, and a practical setup like vLLM with Nginx on AI Hosting GPUs, you can run your own OpenAI‑style endpoints with high performance and full ownership of your data and configuration.

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