What is the best stack for Private AI?

For enterprise privacy, we recommend a stack using AWS Bedrock (governance), Qdrant (Vector DB), Llama 3 (Model), and FastAPI (Backend). This ensures data never leaves your controlled VPC.

How much does it cost to run a private RAG system?

Running a production-grade private RAG system typically costs between $2,000 - $5,000/month in infrastructure fees depending on GPU usage (e.g., g5.xlarge instances) and token volume.

Why use Open Source models like Llama 3 over GPT-4?

Open Source models allow for total data sovereignty. You can fine-tune them on your proprietary data without sharing that data with OpenAI, and you can host them within your own firewall.

TECHNICAL ARCHITECTURE GUIDE

How to Build Private AI & RAG Systems

A blueprint for enterprises demanding data sovereignty. Move beyond public ChatGPT wrappers and build secure, private intelligence.

Last Updated: Dec 2025 • Read time: 10 min

TL;DR

To build a private AI system that doesn't leak data, you must own the model hosting and the vector database.

Model: Host Llama 3 or Mistral on AWS Bedrock or private EC2/GPUs.
Database: Use Qdrant or Milvus for vector storage within your VPC.
Governance: Implement strict IAM roles and disable model training on your data inputs.

Why Public APIs Are Not Enough

Enterprises cannot simply paste customer PII or proprietary IP into public LLM interfaces. The risk of data leakage or model training usage is too high for regulated industries (Finance, Healthcare, Legal).

The Solution: A Retrieval-Augmented Generation (RAG) architecture where the knowledge base lives in your private database, and the LLM acts only as a reasoning engine, hosted in a secure enclave.

RECOMMENDED PRIVATE STACK

Frontend

React / Streamlit

Auth0 / Cognito

→

Orchestration

FastAPI / LangChain

Private Subnet

↔

Knowledge

Qdrant Vector DB

Encrypted at Rest

↔

Inference

AWS Bedrock / GPU

Llama 3 70B

Core Components Breakdown

1. The Vector Database

This is the "Long Term Memory" of your AI. It stores your PDFs, documentation, and client history as mathematical vectors.

Recommendation: Qdrant (Open Source, High Performance) or pgvector (if you already use Postgres).

2. The Inference Engine

Instead of calling `api.openai.com`, you route requests to a model you control.

Recommendation: AWS Bedrock offers the best balance of privacy and ease of use. For total air-gap, run vLLM on EC2 instances.

Frequently Asked Questions

What is the latency like?

Private hosting can actually be faster than public APIs. A well-tuned Llama 3 8B model on a g5.xlarge can output 80+ tokens per second.

Do I need a dedicated AI team?

Building the infrastructure requires DevOps + AI skills. Maintaining it requires SRE. This is where Tech Ops Asia's dedicated teams excel.

CITATIONS & RESOURCES

Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (Lewis et al.)
AWS Bedrock Security Documentation
Tech Ops Asia Brand Facts

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