On Prem vs Cloud AI Server Deployment
Published
Imagine Chat GPT, but local and private.
Many businesses rely on LLMs (Large Language Models, like Chat GPT) to improve productivity and handle repetitive tasks like analyzing files or writting documentation. The problem is that any data given to public services is subject for the company running said service to use, store, and sometimes retrain models with it.
For the average consumer that might not be a big deal, but for businesses that handle sensitive data (banking info, legal docs, medical records), it is a huge liability.
On Premises deployment:
The AI model runs on your own hardware, inside your office. This is usually the best option for privacy and reliability, because your team can access it without the need of an internet connection or third party services.
Cloud deployment:
The model runs on a managed server (like AWS). This option is less private because traffic passes through third party infrastructure, but it is still more private than public LLM services, because the instance is dedicated to your business only.
What does "we expose an API" mean?
It means your tools can talk to the model directly. Your team can integrate the AI into existing workflows instead of using a random chat website.
Bottom line
If privacy matters, on prem is the best. If you want a faster start without buying hardware, cloud can work as a stepping stone.