Open Source vs. GPT-4: The Enterprise Privacy Debate

The Data Sovereignty Question

For many of our Nordic banking and healthcare clients, sending data to OpenAI's US servers—even with an Enterprise license—is a regulatory gray area. This has driven a massive surge in interest for Open Source LLMs.

The Performance Gap is Closing

A year ago, the gap between GPT-4 and open models was a canyon. Today, it is a crack. Models like Llama 3 and Mixtral 8x7B offer reasoning capabilities that rival proprietary models for 90% of standard business tasks (summarization, extraction, classification).

Why Self-Host?

• GDPR & Privacy: Running a model on your own VPC (Virtual Private Cloud) or on-prem hardware means customer data never leaves your controlled environment.
• Cost at Scale: If you are processing millions of tokens a day, paying API fees to a provider becomes unsustainable. Renting a GPU and running a quantized model can cut costs by 10x.
• Latency: No network round-trips to an external API.

The "Small Model" Strategy

You don't always need a genius. You don't hire a PhD to file paperwork. Similarly, we often architecture systems where a "Small" local model handles the bulk of simple tasks, and we only route to GPT-4 for the most complex reasoning problems.