How to Deploy an LLM on AWS EC2 with Your Company Data—Securely

Imagine having your own private ChatGPT-like assistant that knows everything about your company—sales targets, HR policies, project updates—all without ever leaking a byte of data to the outside world. Sounds great, right? With AWS EC2 and a bit of elbow grease, you can make this a reality. In this blog, I’ll walk you through how to deploy a large language model (LLM) on an EC2 instance, load it with your company’s data (think Word docs, Excel sheets, presentations, and more), and keep it all secure within your private cloud environment.

Let’s dive in!

Why Self-Host an LLM?

Public LLMs like ChatGPT are amazing, but they’re not built for sensitive company data. Sending proprietary info to external APIs is a no-go for most businesses. Instead, hosting your own LLM gives you control, privacy, and customization. With AWS EC2, you can build a scalable, secure solution tailored to your internal needs—whether it’s answering “What’s our Q1 revenue goal?” or “Where’s the latest employee handbook?”

Here’s how to do it, step by step.

Step 1: Spin Up an EC2 Instance

First things first—you’ll need a home for your LLM. AWS EC2 is perfect for this. Here’s what to consider:

• Instance Type: If you’ve got a GPU budget, go for something like a g5.xlarge or p3.2xlarge—LLMs love GPU power for fast inference. On a tighter budget? A beefy CPU instance like m5.xlarge can still get the job done for smaller models.
• Storage: Attach an EBS volume (100-500 GB should do) to store your model, data, and indexes.
• Networking: Place the instance in a private subnet within a Virtual Private Cloud (VPC). Lock it down with security groups so only your team (via VPN or internal IPs) can access it.

Think of this as your AI fortress—nothing gets in or out without your say-so.

Step 2: Pick Your LLM

You don’t need to build an LLM from scratch—open-source models have you covered. Some great options:

• LLaMA-3: Efficient and powerful, perfect for enterprise use.
• Mistral: Lightweight yet capable.
• Falcon: Another solid choice for self-hosting.

Grab the model weights from Hugging Face, install Python and PyTorch on your EC2 instance, and load it up. If you’re resource-constrained, tools like llama.cpp can run models on CPUs with minimal fuss.

Step 3: Wrangle Your Company Data

Your company data is probably a glorious mess of Word docs, Excel sheets, PowerPoint slides, and maybe some Google Docs. To make it LLM-ready:

• Extract Text: Word: Use python-docx or pandoc. Excel: pandas can pull text from .xlsx files. PPT: python-pptx extracts slide content. Google Docs: Export as .docx or text via the Drive API.
• Unify It: Convert everything into plain text or structured JSON.
• Clean It: Strip out junk like headers or metadata you don’t need.

Now you’ve got a tidy pile of text ready to feed into your system.

Step 4: Build a Secure Knowledge Base

Here’s the magic trick: instead of fine-tuning the LLM (which takes tons of time and compute), use Retrieval-Augmented Generation (RAG). This lets your LLM pull answers from your data on the fly.

• Vector Database: Install something like FAISS or Chroma locally on your EC2 instance. These tools turn your text into searchable embeddings (think of them as AI-friendly fingerprints).
• Embeddings: Use a model like sentence-transformers to convert your docs into vectors.
• Keep It Local: Store everything on the instance—no cloud syncing, no external calls.

When someone asks a question, the system finds the right doc and hands it to the LLM as context. Simple, secure, and effective.

Step 5: Wire It All Together

With your LLM and data ready, connect them using a framework like LangChain or Haystack. Here’s how it works:

1. User asks: “What’s our latest HR policy update?”
2. Query gets turned into an embedding.
3. Vector database fetches the relevant HR doc.
4. LLM crafts an answer: “The latest update, effective Jan 2025, adds a $50/month remote work allowance.”

No data leaves your EC2 instance—everything happens in-house.

Step 6: Lock It Down

Security is non-negotiable. Here’s how to keep your fortress safe:

• Firewall: Use security groups to restrict access to internal traffic only.
• Encryption: Encrypt your EBS volume with AWS KMS.
• No Internet: Pre-download models and tools—don’t let the instance phone home during runtime.
• Access: Tie it to an IAM role with minimal permissions.

Step 7: Make It Usable

You’ve got the brains—now add a face:

• API: Set up a REST API with Flask or FastAPI. A simple POST /ask endpoint can handle queries.
• Authentication: Add API keys or OAuth for internal users.
• UI (Optional): Spin up a quick Streamlit app for a browser-based chat interface.

Step 8: Test and Grow

Upload some sample data, ask a few questions (“What’s our sales target?”), and tweak as needed. If your team loves it, scale up with an Auto Scaling group or load balancer—all within your VPC.

Why This Works

This setup keeps your data private—no external APIs, no leaks. It’s customizable to your company’s needs and runs on hardware you control. Plus, with RAG, you don’t need to retrain the LLM every time a new doc lands—just index it and go.

Tools to Get Started

• LLM: LLaMA-3, Mistral (via Hugging Face)
• Text Extraction: python-docx, pandas, python-pptx
• Vector Store: FAISS, Chroma
• RAG: LangChain, Haystack
• AWS: EC2, VPC, EBS

Final Thoughts

Building your own company-specific LLM on AWS EC2 is like giving your team a super-smart, super-private assistant. It takes some setup, but the payoff—secure, instant answers to internal questions—is worth it. Got a pile of docs and an EC2 instance handy? You’re halfway there.

What do you think—ready to give it a shot ?